Agglomerative hierarchical is a bottom up clustering method, where the distances between documents can be retrieved by extracting feature values using a topic-based latent dirichlet allocation method. To reduce the number of features, term selection can be done using Luhn’s Idea. Those methods can be used to build the better clusters for document. But, there is less research discusses it. Therefore, in this research, the term weighting calculation uses Luhn’s Idea to select the terms by defining upper and lower cut-off, and then extracts the feature of terms using gibbs sampling latent dirichlet allocation combined with term frequency and fuzzy Sugeno method. The feature values used to be the distance between documents, and clustered with single, complete and average link algorithm. The evaluations show the feature extraction with and without lower cut-off have less difference. But, the topic determination of each term based on term frequency and fuzzy Sugeno method is better than Tsukamoto method in finding more relevant documents. The used of lower cut-off and fuzzy Sugeno gibbs latent dirichlet allocation for complete agglomerative hierarchical clustering have consistent metric values. This clustering method suggested as a better method in clustering documents that is more relevant to its gold standard.
This document summarizes an article that proposes an automatic text summarization technique using feature terms to calculate sentence relevance. The technique uses both statistical and linguistic methods to identify semantically important sentences for creating a generic summary. It determines the relevance of sentences based on feature term ranks and performs semantic analysis of sentences with the highest ranks to select those most important for the summary. The performance is evaluated by comparing summaries to those created by human evaluators.
An Improved Similarity Matching based Clustering Framework for Short and Sent...IJECEIAES
Text clustering plays a key role in navigation and browsing process. For an efficient text clustering, the large amount of information is grouped into meaningful clusters. Multiple text clustering techniques do not address the issues such as, high time and space complexity, inability to understand the relational and contextual attributes of the word, less robustness, risks related to privacy exposure, etc. To address these issues, an efficient text based clustering framework is proposed. The Reuters dataset is chosen as the input dataset. Once the input dataset is preprocessed, the similarity between the words are computed using the cosine similarity. The similarities between the components are compared and the vector data is created. From the vector data the clustering particle is computed. To optimize the clustering results, mutation is applied to the vector data. The performance the proposed text based clustering framework is analyzed using the metrics such as Mean Square Error (MSE), Peak Signal Noise Ratio (PSNR) and Processing time. From the experimental results, it is found that, the proposed text based clustering framework produced optimal MSE, PSNR and processing time when compared to the existing Fuzzy C-Means (FCM) and Pairwise Random Swap (PRS) methods.
This document discusses using the Expectation Maximization clustering algorithm for text summarization. It begins with an introduction to text summarization and natural language processing. It then describes implementing Expectation Maximization clustering on text that has undergone natural language processing steps like splitting, tokenization, part-of-speech tagging, and parsing. This clusters similar sentences based on their similarity values from the distance matrix. The clustered sentences can then be used to generate a summary by selecting the most representative sentences.
A rough set based hybrid method to text categorizationNinad Samel
This document summarizes a hybrid text categorization method that combines Latent Semantic Indexing (LSI) and Rough Sets theory to reduce the dimensionality of text data and generate classification rules. It introduces LSI to reduce the feature space of text documents represented as high-dimensional vectors. Then it applies Rough Sets theory to the reduced feature space to locate a minimal set of keywords that can distinguish document classes and generate multiple knowledge bases for classification instead of a single one. The method is tested on text categorization tasks and shown to improve accuracy over previous Rough Sets approaches.
A Newly Proposed Technique for Summarizing the Abstractive Newspapers’ Articl...mlaij
In this new era, where tremendous information is available on the internet, it is of most important to
provide the improved mechanism to extract the information quickly and most efficiently. It is very difficult
for human beings to manually extract the summary of large documents of text. Therefore, there is a
problem of searching for relevant documents from the number of documents available, and absorbing
relevant information from it. In order to solve the above two problems, the automatic text summarization is
very much necessary. Text summarization is the process of identifying the most important meaningful
information in a document or set of related documents and compressing them into a shorter version
preserving its overall meanings. More specific, Abstractive Text Summarization (ATS), is the task of
constructing summary sentences by merging facts from different source sentences and condensing them
into a shorter representation while preserving information content and overall meaning. This Paper
introduces a newly proposed technique for Summarizing the abstractive newspapers’ articles based on
deep learning.
Correlation Preserving Indexing Based Text ClusteringIOSR Journals
This document discusses a correlation preserving indexing (CPI) based text clustering method. CPI aims to find a low dimensional semantic subspace that maximizes correlation between similar documents while minimizing correlation between dissimilar documents. It is different from other methods like LSI and LPI that use Euclidean distance. The document outlines the CPI method and evaluates it on document clustering tasks, showing it doubles the accuracy of previous correlation-based methods. Hierarchical clustering algorithms are also discussed and compared to CPI in terms of evaluation metrics.
An Enhanced Suffix Tree Approach to Measure Semantic Similarity between Multi...iosrjce
1) The document discusses an approach to measure semantic similarity between multiple documents using an enhanced suffix tree. It involves preprocessing documents, constructing a suffix tree with documents' phrases as edges, calculating weights of shared nodes using TF-IDF, and applying cosine, dice, and hellinger similarity measures to determine pairwise document similarities.
2) The approach first preprocesses documents by removing stop words, special characters, and converting to lowercase. A suffix tree is constructed with documents' phrases as edges. Shared nodes in the tree represent common phrases between documents.
3) Node weights are calculated using TF-IDF, with higher weights given to rarer phrases. Several similarity measures (cosine, dice, hellinger) are then applied
A comparative analysis of particle swarm optimization and k means algorithm f...ijnlc
The volume of digitized text documents on the web have been increasing rapidly. As there is huge collection
of data on the web there is a need for grouping(clustering) the documents into clusters for speedy
information retrieval. Clustering of documents is collection of documents into groups such that the
documents within each group are similar to each other and not to documents of other groups. Quality of
clustering result depends greatly on the representation of text and the clustering algorithm. This paper
presents a comparative analysis of three algorithms namely K-means, Particle swarm Optimization (PSO)
and hybrid PSO+K-means algorithm for clustering of text documents using WordNet. The common way of
representing a text document is bag of terms. The bag of terms representation is often unsatisfactory as it
does not exploit the semantics. In this paper, texts are represented in terms of synsets corresponding to a
word. Bag of terms data representation of text is thus enriched with synonyms from WordNet. K-means,
Particle Swarm Optimization (PSO) and hybrid PSO+K-means algorithms are applied for clustering of
text in Nepali language. Experimental evaluation is performed by using intra cluster similarity and inter
cluster similarity.
This document summarizes an article that proposes an automatic text summarization technique using feature terms to calculate sentence relevance. The technique uses both statistical and linguistic methods to identify semantically important sentences for creating a generic summary. It determines the relevance of sentences based on feature term ranks and performs semantic analysis of sentences with the highest ranks to select those most important for the summary. The performance is evaluated by comparing summaries to those created by human evaluators.
An Improved Similarity Matching based Clustering Framework for Short and Sent...IJECEIAES
Text clustering plays a key role in navigation and browsing process. For an efficient text clustering, the large amount of information is grouped into meaningful clusters. Multiple text clustering techniques do not address the issues such as, high time and space complexity, inability to understand the relational and contextual attributes of the word, less robustness, risks related to privacy exposure, etc. To address these issues, an efficient text based clustering framework is proposed. The Reuters dataset is chosen as the input dataset. Once the input dataset is preprocessed, the similarity between the words are computed using the cosine similarity. The similarities between the components are compared and the vector data is created. From the vector data the clustering particle is computed. To optimize the clustering results, mutation is applied to the vector data. The performance the proposed text based clustering framework is analyzed using the metrics such as Mean Square Error (MSE), Peak Signal Noise Ratio (PSNR) and Processing time. From the experimental results, it is found that, the proposed text based clustering framework produced optimal MSE, PSNR and processing time when compared to the existing Fuzzy C-Means (FCM) and Pairwise Random Swap (PRS) methods.
This document discusses using the Expectation Maximization clustering algorithm for text summarization. It begins with an introduction to text summarization and natural language processing. It then describes implementing Expectation Maximization clustering on text that has undergone natural language processing steps like splitting, tokenization, part-of-speech tagging, and parsing. This clusters similar sentences based on their similarity values from the distance matrix. The clustered sentences can then be used to generate a summary by selecting the most representative sentences.
A rough set based hybrid method to text categorizationNinad Samel
This document summarizes a hybrid text categorization method that combines Latent Semantic Indexing (LSI) and Rough Sets theory to reduce the dimensionality of text data and generate classification rules. It introduces LSI to reduce the feature space of text documents represented as high-dimensional vectors. Then it applies Rough Sets theory to the reduced feature space to locate a minimal set of keywords that can distinguish document classes and generate multiple knowledge bases for classification instead of a single one. The method is tested on text categorization tasks and shown to improve accuracy over previous Rough Sets approaches.
A Newly Proposed Technique for Summarizing the Abstractive Newspapers’ Articl...mlaij
In this new era, where tremendous information is available on the internet, it is of most important to
provide the improved mechanism to extract the information quickly and most efficiently. It is very difficult
for human beings to manually extract the summary of large documents of text. Therefore, there is a
problem of searching for relevant documents from the number of documents available, and absorbing
relevant information from it. In order to solve the above two problems, the automatic text summarization is
very much necessary. Text summarization is the process of identifying the most important meaningful
information in a document or set of related documents and compressing them into a shorter version
preserving its overall meanings. More specific, Abstractive Text Summarization (ATS), is the task of
constructing summary sentences by merging facts from different source sentences and condensing them
into a shorter representation while preserving information content and overall meaning. This Paper
introduces a newly proposed technique for Summarizing the abstractive newspapers’ articles based on
deep learning.
Correlation Preserving Indexing Based Text ClusteringIOSR Journals
This document discusses a correlation preserving indexing (CPI) based text clustering method. CPI aims to find a low dimensional semantic subspace that maximizes correlation between similar documents while minimizing correlation between dissimilar documents. It is different from other methods like LSI and LPI that use Euclidean distance. The document outlines the CPI method and evaluates it on document clustering tasks, showing it doubles the accuracy of previous correlation-based methods. Hierarchical clustering algorithms are also discussed and compared to CPI in terms of evaluation metrics.
An Enhanced Suffix Tree Approach to Measure Semantic Similarity between Multi...iosrjce
1) The document discusses an approach to measure semantic similarity between multiple documents using an enhanced suffix tree. It involves preprocessing documents, constructing a suffix tree with documents' phrases as edges, calculating weights of shared nodes using TF-IDF, and applying cosine, dice, and hellinger similarity measures to determine pairwise document similarities.
2) The approach first preprocesses documents by removing stop words, special characters, and converting to lowercase. A suffix tree is constructed with documents' phrases as edges. Shared nodes in the tree represent common phrases between documents.
3) Node weights are calculated using TF-IDF, with higher weights given to rarer phrases. Several similarity measures (cosine, dice, hellinger) are then applied
A comparative analysis of particle swarm optimization and k means algorithm f...ijnlc
The volume of digitized text documents on the web have been increasing rapidly. As there is huge collection
of data on the web there is a need for grouping(clustering) the documents into clusters for speedy
information retrieval. Clustering of documents is collection of documents into groups such that the
documents within each group are similar to each other and not to documents of other groups. Quality of
clustering result depends greatly on the representation of text and the clustering algorithm. This paper
presents a comparative analysis of three algorithms namely K-means, Particle swarm Optimization (PSO)
and hybrid PSO+K-means algorithm for clustering of text documents using WordNet. The common way of
representing a text document is bag of terms. The bag of terms representation is often unsatisfactory as it
does not exploit the semantics. In this paper, texts are represented in terms of synsets corresponding to a
word. Bag of terms data representation of text is thus enriched with synonyms from WordNet. K-means,
Particle Swarm Optimization (PSO) and hybrid PSO+K-means algorithms are applied for clustering of
text in Nepali language. Experimental evaluation is performed by using intra cluster similarity and inter
cluster similarity.
Computational Intelligence Methods for Clustering of Sense Tagged Nepali Docu...IOSR Journals
The document describes a hybrid computational intelligence method for clustering sense-tagged Nepali documents. It combines self-organizing maps (SOM), particle swarm optimization (PSO), and k-means clustering. Feature vectors are generated from the sense-tagged documents and the hybrid algorithm is applied in three phases: (1) SOM produces prototype vectors from the feature vectors, (2) PSO initializes k-means centroids, (3) k-means clusters the prototypes. The method aims to address limitations of bag-of-words representations by incorporating word sense information. Experiments show the approach effectively clusters sense-tagged Nepali texts.
1) The document discusses different clustering algorithms for text summarization including hierarchical clustering, query-based summarization, graph theoretic clustering, fuzzy c-means clustering, and DBSCAN clustering.
2) These algorithms are evaluated based on performance parameters like precision, recall, time complexity, space complexity, and summary quality.
3) The algorithm found to perform best based on these evaluations will be suggested as the better algorithm for query-dependent text document summarization.
A NOVEL APPROACH FOR WORD RETRIEVAL FROM DEVANAGARI DOCUMENT IMAGESijnlc
Large amount of information is lying dormant in historical documents and manuscripts. This information would go futile if not stored in digital form. Searching some relevant information from these scanned images would ideally require converting these document images to text form by doing optical character
recognition (OCR). For indigenous scripts of India, there are very few OCRs that can successfully recognize printed text images of varying quality, size, style and font. An alternate approach using word spotting can be effective to access large collections of document images. We propose a word spotting
technique based on codes for matching the word images of Devanagari script. The shape information is utilised for generating integer codes for words in the document image and these codes are matched for final retrieval of relevant documents. The technique is illustrated using Marathi document images.
Query Answering Approach Based on Document SummarizationIJMER
The growing of online information obliged the availability of a thorough research in the
domain of automatic text summarization within the Natural Language Processing (NLP)
community.The aim of this paper is to propose a novel approach for a language independent automatic
summarization approach that combines three main approaches. The Rhetorical Structure Theory
(RST), the query processing approach, and the Network Representationapproach (NRA). RST, as a
theory of major aspect for the structure of natural text, is used to extract the semantic relation behind
the text.Query processing approachclassifies the question type and finds the answer in a way that suits
the user’s needs. The NRA is used to create a graph representing the extracted semantic relation. The
output is an answer, which not only responses to the question, but also gives the user an opportunity to
find additional information that is related to the question.We implemented the proposed approach. As a
case study, the implemented approachis applied on Arabic text in the agriculture field. The
implemented approach succeeded in summarizing extension documents according to user's query. The
approach results have been evaluated using Recall, Precision and F-score measures.
This document describes a proposed concept-based mining model that aims to improve document clustering and information retrieval by extracting concepts and semantic relationships rather than just keywords. The model uses natural language processing techniques like part-of-speech tagging and parsing to extract concepts from text. It represents concepts and their relationships in a semantic network and clusters documents based on conceptual similarity rather than term frequency. The model is evaluated using singular value decomposition to increase the precision of key term and phrase extraction.
A Comparative Study of Centroid-Based and Naïve Bayes Classifiers for Documen...IJERA Editor
Assigning documents to related categories is critical task which is used for effective document retrieval. Automatic text classification is the process of assigning new text document to the predefined categories based on its content. In this paper, we implemented and performed comparison of Naïve Bayes and Centroid-based algorithms for effective document categorization of English language text. In Centroid Based algorithm, we used Arithmetical Average Centroid (AAC) and Cumuli Geometric Centroid (CGC) methods to calculate centroid of each class. Experiment is performed on R-52 dataset of Reuters-21578 corpus. Micro Average F1 measure is used to evaluate the performance of classifiers. Experimental results show that Micro Average F1 value for NB is greatest among all followed by Micro Average F1 value of CGC which is greater than Micro Average F1 of AAC. All these results are valuable for future research
This paper proposes Natural language based Discourse Analysis method used for extracting
information from the news article of different domain. The Discourse analysis used the Rhetorical Structure
theory which is used to find coherent group of text which are most prominent for extracting information
from text. RST theory used the Nucleus- Satellite concept for finding most prominent text from the text
document. After Discourse analysis the text analysis has been done for extracting domain related object
and relates this object. For extracting the information knowledge based system has been used which
consist of domain dictionary .The domain dictionary has a bag of words for domain. The system is
evaluated according gold-of-art analysis and human decision for extracted information.
Farthest Neighbor Approach for Finding Initial Centroids in K- MeansWaqas Tariq
Text document clustering is gaining popularity in the knowledge discovery field for effectively navigating, browsing and organizing large amounts of textual information into a small number of meaningful clusters. Text mining is a semi-automated process of extracting knowledge from voluminous unstructured data. A widely studied data mining problem in the text domain is clustering. Clustering is an unsupervised learning method that aims to find groups of similar objects in the data with respect to some predefined criterion. In this work we propose a variant method for finding initial centroids. The initial centroids are chosen by using farthest neighbors. For the partitioning based clustering algorithms traditionally the initial centroids are chosen randomly but in the proposed method the initial centroids are chosen by using farthest neighbors. The accuracy of the clusters and efficiency of the partition based clustering algorithms depend on the initial centroids chosen. In the experiment, kmeans algorithm is applied and the initial centroids for kmeans are chosen by using farthest neighbors. Our experimental results shows the accuracy of the clusters and efficiency of the kmeans algorithm is improved compared to the traditional way of choosing initial centroids.
Text mining is a new and exciting research area that tries to solve the information overload problem by using techniques from machine learning, natural language processing (NLP), data mining, information retrieval (IR), and knowledge management. Text mining involves the pre-processing of document collections such as information extraction, term extraction, text categorization, and storage of intermediate representations. The techniques that are used to analyse these intermediate representations such as clustering, distribution analysis, association rules and visualisation of the results.
A template based algorithm for automatic summarization and dialogue managemen...eSAT Journals
Abstract This paper describes an automated approach for extracting significant and useful events from unstructured text. The goal of research is to come out with a methodology which helps in extracting important events such as dates, places, and subjects of interest. It would be also convenient if the methodology helps in presenting the users with a shorter version of the text which contain all non-trivial information. We also discuss implementation of algorithms which exactly does this task, developed by us. Key Words: Cosine Similarity, Information, Natural Language, Summarization, Text Mining
This document summarizes a research paper that introduces a novel multi-viewpoint similarity measure for clustering text documents. The paper begins with background on commonly used similarity measures like Euclidean distance and cosine similarity. It then presents the novel multi-viewpoint measure, which considers multiple viewpoints (objects not assumed to be in the same cluster) rather than a single viewpoint. The paper proposes two new clustering criterion functions based on this measure and compares them to other algorithms on benchmark datasets. The goal is to develop a similarity measure and clustering methods that provide high-quality, consistent performance like k-means but can better handle sparse, high-dimensional text data.
An automatic text summarization using lexical cohesion and correlation of sen...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Optimisation towards Latent Dirichlet Allocation: Its Topic Number and Collap...IJECEIAES
Latent Dirichlet Allocation (LDA) is a probability model for grouping hidden topics in documents by the number of predefined topics. If conducted incorrectly, determining the amount of K topics will result in limited word correlation with topics. Too large or too small number of K topics causes inaccuracies in grouping topics in the formation of training models. This study aims to determine the optimal number of corpus topics in the LDA method using the maximum likelihood and Minimum Description Length (MDL) approach. The experimental process uses Indonesian news articles with the number of documents at 25, 50, 90, and 600; in each document, the numbers of words are 3898, 7760, 13005, and 4365. The results show that the maximum likelihood and MDL approach result in the same number of optimal topics. The optimal number of topics is influenced by alpha and beta parameters. In addition, the number of documents does not affect the computation times but the number of words does. Computational times for each of those datasets are 2.9721, 6.49637, 13.2967, and 3.7152 seconds. The optimisation model has resulted in many LDA topics as a classification model. This experiment shows that the highest average accuracy is 61% with alpha 0.1 and beta 0.001.
8 efficient multi-document summary generation using neural networkINFOGAIN PUBLICATION
This paper proposes a multi-document summarization system that uses bisect k-means clustering, an optimal merge function, and a neural network. The system first preprocesses input documents through stemming and removing stop words. It then applies bisect k-means clustering to group similar sentences. The clusters are merged using an optimal merge function to find important keywords. The NEWSUM algorithm is used to generate a primary summary for each keyword. A neural network trained on sentence classifications is then used to classify sentences in the primary summary as positive or negative. Only positively classified sentences are included in the final summary to improve accuracy. The system aims to generate a concise and accurate summary in a short period of time from multiple documents on a given topic.
DOCUMENT SUMMARIZATION IN KANNADA USING KEYWORD EXTRACTION cscpconf
The internet has caused a humongous growth in the amount of data available to the common
man. Summaries of documents can help find the right information and are particularly effective
when the document base is very large. Keywords are closely associated to a document as they
reflect the document's content and act as indexes for the given document. In this work, we
present a method to produce extractive summaries of documents in the Kannada language. The
algorithm extracts key words from pre-categorized Kannada documents collected from online
resources. We combine GSS (Galavotti, Sebastiani, Simi) coefficients and IDF (Inverse
Document Frequency) methods along with TF (Term Frequency) for extracting key words and
later use these for summarization. In the current implementation a document from a given category is selected from our database and depending on the number of sentences given by theuser, a summary is generated.
Suitability of naïve bayesian methods for paragraph level text classification...ijaia
This document discusses using Naive Bayesian methods for paragraph-level text classification in the Kannada language. It evaluates the performance of the Naive Bayesian and Naive Bayesian Multinomial models on a corpus of 1791 paragraphs from four categories (Commerce, Social Sciences, Natural Sciences, Aesthetics). Dimensionality reduction techniques like removing stop words and words with low term frequency are applied before classification. The results show that the Naive Bayesian Multinomial model outperforms the simple Naive Bayesian approach for paragraph classification in Kannada.
Rhetorical Sentence Classification for Automatic Title Generation in Scientif...TELKOMNIKA JOURNAL
In this paper, we proposed a work on rhetorical corpus construction and sentence classification
model experiment that specifically could be incorporated in automatic paper title generation task for
scientific article. Rhetorical classification is treated as sequence labeling. Rhetorical sentence classification
model is useful in task which considers document’s discourse structure. We performed experiments using
two domains of datasets: computer science (CS dataset), and chemistry (GaN dataset). We evaluated the
models using 10-fold-cross validation (0.70-0.79 weighted average F-measure) as well as on-the-run
(0.30-0.36 error rate at best). We argued that our models performed best when handled using SMOTE
filter for imbalanced data.
The document describes latent Dirichlet allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora. LDA represents documents as random mixtures over latent topics, characterized by distributions over words. It is a three-level hierarchical Bayesian model where documents are generated by first sampling a per-document topic distribution from a Dirichlet prior, then repeatedly sampling topics and words from these distributions. LDA addresses limitations of previous models by capturing statistical structure within and between documents through the hierarchical Bayesian formulation.
Improvement of Text Summarization using Fuzzy Logic Based MethodIOSR Journals
The document describes a method for improving text summarization using fuzzy logic. It proposes using fuzzy logic to determine the importance of sentences based on calculated feature scores. Eight features are used to score sentences, including title words, length, term frequency, position, and similarity. Sentences are then ranked based on their fuzzy logic-determined scores. The highest scoring sentences are extracted to create a summary. An evaluation of summaries generated using this fuzzy logic method found it performed better than other summarizers in accurately reflecting the content and order of human-generated reference summaries. The method could be expanded to multi-document summarization and automatic selection of fuzzy rules based on input type.
Feature selection, optimization and clustering strategies of text documentsIJECEIAES
Clustering is one of the most researched areas of data mining applications in the contemporary literature. The need for efficient clustering is observed across wide sectors including consumer segmentation, categorization, shared filtering, document management, and indexing. The research of clustering task is to be performed prior to its adaptation in the text environment. Conventional approaches typically emphasized on the quantitative information where the selected features are numbers. Efforts also have been put forward for achieving efficient clustering in the context of categorical information where the selected features can assume nominal values. This manuscript presents an in-depth analysis of challenges of clustering in the text environment. Further, this paper also details prominent models proposed for clustering along with the pros and cons of each model. In addition, it also focuses on various latest developments in the clustering task in the social network and associated environments.
Semantics-based clustering approach for similar research area detectionTELKOMNIKA JOURNAL
The manual process of searching out individuals in an already existing
research field is cumbersome and time-consuming. Prominent and rookie
researchers alike are predisposed to seek existing research publications in
a research field of interest before coming up with a thesis. From
extant literature, automated similar research area detection systems have
been developed to solve this problem. However, most of them use
keyword-matching techniques, which do not sufficiently capture the implicit
semantics of keywords thereby leaving out some research articles. In this
study, we propose the use of ontology-based pre-processing, Latent Semantic
Indexing and K-Means Clustering to develop a prototype similar research area
detection system, that can be used to determine similar research domain
publications. Our proposed system solves the challenge of high dimensionality
and data sparsity faced by the traditional document clustering technique. Our
system is evaluated with randomly selected publications from faculties
in Nigerian universities and results show that the integration of ontologies
in preprocessing provides more accurate clustering results.
This document discusses hierarchical clustering and similarity measures for document clustering. It summarizes that hierarchical clustering creates a hierarchical decomposition of data objects through either agglomerative or divisive approaches. The success of clustering depends on the similarity measure used, with traditional measures using a single viewpoint, while multiviewpoint measures use different viewpoints to increase accuracy. The paper then focuses on applying a multiviewpoint similarity measure to hierarchical clustering of documents.
Computational Intelligence Methods for Clustering of Sense Tagged Nepali Docu...IOSR Journals
The document describes a hybrid computational intelligence method for clustering sense-tagged Nepali documents. It combines self-organizing maps (SOM), particle swarm optimization (PSO), and k-means clustering. Feature vectors are generated from the sense-tagged documents and the hybrid algorithm is applied in three phases: (1) SOM produces prototype vectors from the feature vectors, (2) PSO initializes k-means centroids, (3) k-means clusters the prototypes. The method aims to address limitations of bag-of-words representations by incorporating word sense information. Experiments show the approach effectively clusters sense-tagged Nepali texts.
1) The document discusses different clustering algorithms for text summarization including hierarchical clustering, query-based summarization, graph theoretic clustering, fuzzy c-means clustering, and DBSCAN clustering.
2) These algorithms are evaluated based on performance parameters like precision, recall, time complexity, space complexity, and summary quality.
3) The algorithm found to perform best based on these evaluations will be suggested as the better algorithm for query-dependent text document summarization.
A NOVEL APPROACH FOR WORD RETRIEVAL FROM DEVANAGARI DOCUMENT IMAGESijnlc
Large amount of information is lying dormant in historical documents and manuscripts. This information would go futile if not stored in digital form. Searching some relevant information from these scanned images would ideally require converting these document images to text form by doing optical character
recognition (OCR). For indigenous scripts of India, there are very few OCRs that can successfully recognize printed text images of varying quality, size, style and font. An alternate approach using word spotting can be effective to access large collections of document images. We propose a word spotting
technique based on codes for matching the word images of Devanagari script. The shape information is utilised for generating integer codes for words in the document image and these codes are matched for final retrieval of relevant documents. The technique is illustrated using Marathi document images.
Query Answering Approach Based on Document SummarizationIJMER
The growing of online information obliged the availability of a thorough research in the
domain of automatic text summarization within the Natural Language Processing (NLP)
community.The aim of this paper is to propose a novel approach for a language independent automatic
summarization approach that combines three main approaches. The Rhetorical Structure Theory
(RST), the query processing approach, and the Network Representationapproach (NRA). RST, as a
theory of major aspect for the structure of natural text, is used to extract the semantic relation behind
the text.Query processing approachclassifies the question type and finds the answer in a way that suits
the user’s needs. The NRA is used to create a graph representing the extracted semantic relation. The
output is an answer, which not only responses to the question, but also gives the user an opportunity to
find additional information that is related to the question.We implemented the proposed approach. As a
case study, the implemented approachis applied on Arabic text in the agriculture field. The
implemented approach succeeded in summarizing extension documents according to user's query. The
approach results have been evaluated using Recall, Precision and F-score measures.
This document describes a proposed concept-based mining model that aims to improve document clustering and information retrieval by extracting concepts and semantic relationships rather than just keywords. The model uses natural language processing techniques like part-of-speech tagging and parsing to extract concepts from text. It represents concepts and their relationships in a semantic network and clusters documents based on conceptual similarity rather than term frequency. The model is evaluated using singular value decomposition to increase the precision of key term and phrase extraction.
A Comparative Study of Centroid-Based and Naïve Bayes Classifiers for Documen...IJERA Editor
Assigning documents to related categories is critical task which is used for effective document retrieval. Automatic text classification is the process of assigning new text document to the predefined categories based on its content. In this paper, we implemented and performed comparison of Naïve Bayes and Centroid-based algorithms for effective document categorization of English language text. In Centroid Based algorithm, we used Arithmetical Average Centroid (AAC) and Cumuli Geometric Centroid (CGC) methods to calculate centroid of each class. Experiment is performed on R-52 dataset of Reuters-21578 corpus. Micro Average F1 measure is used to evaluate the performance of classifiers. Experimental results show that Micro Average F1 value for NB is greatest among all followed by Micro Average F1 value of CGC which is greater than Micro Average F1 of AAC. All these results are valuable for future research
This paper proposes Natural language based Discourse Analysis method used for extracting
information from the news article of different domain. The Discourse analysis used the Rhetorical Structure
theory which is used to find coherent group of text which are most prominent for extracting information
from text. RST theory used the Nucleus- Satellite concept for finding most prominent text from the text
document. After Discourse analysis the text analysis has been done for extracting domain related object
and relates this object. For extracting the information knowledge based system has been used which
consist of domain dictionary .The domain dictionary has a bag of words for domain. The system is
evaluated according gold-of-art analysis and human decision for extracted information.
Farthest Neighbor Approach for Finding Initial Centroids in K- MeansWaqas Tariq
Text document clustering is gaining popularity in the knowledge discovery field for effectively navigating, browsing and organizing large amounts of textual information into a small number of meaningful clusters. Text mining is a semi-automated process of extracting knowledge from voluminous unstructured data. A widely studied data mining problem in the text domain is clustering. Clustering is an unsupervised learning method that aims to find groups of similar objects in the data with respect to some predefined criterion. In this work we propose a variant method for finding initial centroids. The initial centroids are chosen by using farthest neighbors. For the partitioning based clustering algorithms traditionally the initial centroids are chosen randomly but in the proposed method the initial centroids are chosen by using farthest neighbors. The accuracy of the clusters and efficiency of the partition based clustering algorithms depend on the initial centroids chosen. In the experiment, kmeans algorithm is applied and the initial centroids for kmeans are chosen by using farthest neighbors. Our experimental results shows the accuracy of the clusters and efficiency of the kmeans algorithm is improved compared to the traditional way of choosing initial centroids.
Text mining is a new and exciting research area that tries to solve the information overload problem by using techniques from machine learning, natural language processing (NLP), data mining, information retrieval (IR), and knowledge management. Text mining involves the pre-processing of document collections such as information extraction, term extraction, text categorization, and storage of intermediate representations. The techniques that are used to analyse these intermediate representations such as clustering, distribution analysis, association rules and visualisation of the results.
A template based algorithm for automatic summarization and dialogue managemen...eSAT Journals
Abstract This paper describes an automated approach for extracting significant and useful events from unstructured text. The goal of research is to come out with a methodology which helps in extracting important events such as dates, places, and subjects of interest. It would be also convenient if the methodology helps in presenting the users with a shorter version of the text which contain all non-trivial information. We also discuss implementation of algorithms which exactly does this task, developed by us. Key Words: Cosine Similarity, Information, Natural Language, Summarization, Text Mining
This document summarizes a research paper that introduces a novel multi-viewpoint similarity measure for clustering text documents. The paper begins with background on commonly used similarity measures like Euclidean distance and cosine similarity. It then presents the novel multi-viewpoint measure, which considers multiple viewpoints (objects not assumed to be in the same cluster) rather than a single viewpoint. The paper proposes two new clustering criterion functions based on this measure and compares them to other algorithms on benchmark datasets. The goal is to develop a similarity measure and clustering methods that provide high-quality, consistent performance like k-means but can better handle sparse, high-dimensional text data.
An automatic text summarization using lexical cohesion and correlation of sen...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Optimisation towards Latent Dirichlet Allocation: Its Topic Number and Collap...IJECEIAES
Latent Dirichlet Allocation (LDA) is a probability model for grouping hidden topics in documents by the number of predefined topics. If conducted incorrectly, determining the amount of K topics will result in limited word correlation with topics. Too large or too small number of K topics causes inaccuracies in grouping topics in the formation of training models. This study aims to determine the optimal number of corpus topics in the LDA method using the maximum likelihood and Minimum Description Length (MDL) approach. The experimental process uses Indonesian news articles with the number of documents at 25, 50, 90, and 600; in each document, the numbers of words are 3898, 7760, 13005, and 4365. The results show that the maximum likelihood and MDL approach result in the same number of optimal topics. The optimal number of topics is influenced by alpha and beta parameters. In addition, the number of documents does not affect the computation times but the number of words does. Computational times for each of those datasets are 2.9721, 6.49637, 13.2967, and 3.7152 seconds. The optimisation model has resulted in many LDA topics as a classification model. This experiment shows that the highest average accuracy is 61% with alpha 0.1 and beta 0.001.
8 efficient multi-document summary generation using neural networkINFOGAIN PUBLICATION
This paper proposes a multi-document summarization system that uses bisect k-means clustering, an optimal merge function, and a neural network. The system first preprocesses input documents through stemming and removing stop words. It then applies bisect k-means clustering to group similar sentences. The clusters are merged using an optimal merge function to find important keywords. The NEWSUM algorithm is used to generate a primary summary for each keyword. A neural network trained on sentence classifications is then used to classify sentences in the primary summary as positive or negative. Only positively classified sentences are included in the final summary to improve accuracy. The system aims to generate a concise and accurate summary in a short period of time from multiple documents on a given topic.
DOCUMENT SUMMARIZATION IN KANNADA USING KEYWORD EXTRACTION cscpconf
The internet has caused a humongous growth in the amount of data available to the common
man. Summaries of documents can help find the right information and are particularly effective
when the document base is very large. Keywords are closely associated to a document as they
reflect the document's content and act as indexes for the given document. In this work, we
present a method to produce extractive summaries of documents in the Kannada language. The
algorithm extracts key words from pre-categorized Kannada documents collected from online
resources. We combine GSS (Galavotti, Sebastiani, Simi) coefficients and IDF (Inverse
Document Frequency) methods along with TF (Term Frequency) for extracting key words and
later use these for summarization. In the current implementation a document from a given category is selected from our database and depending on the number of sentences given by theuser, a summary is generated.
Suitability of naïve bayesian methods for paragraph level text classification...ijaia
This document discusses using Naive Bayesian methods for paragraph-level text classification in the Kannada language. It evaluates the performance of the Naive Bayesian and Naive Bayesian Multinomial models on a corpus of 1791 paragraphs from four categories (Commerce, Social Sciences, Natural Sciences, Aesthetics). Dimensionality reduction techniques like removing stop words and words with low term frequency are applied before classification. The results show that the Naive Bayesian Multinomial model outperforms the simple Naive Bayesian approach for paragraph classification in Kannada.
Rhetorical Sentence Classification for Automatic Title Generation in Scientif...TELKOMNIKA JOURNAL
In this paper, we proposed a work on rhetorical corpus construction and sentence classification
model experiment that specifically could be incorporated in automatic paper title generation task for
scientific article. Rhetorical classification is treated as sequence labeling. Rhetorical sentence classification
model is useful in task which considers document’s discourse structure. We performed experiments using
two domains of datasets: computer science (CS dataset), and chemistry (GaN dataset). We evaluated the
models using 10-fold-cross validation (0.70-0.79 weighted average F-measure) as well as on-the-run
(0.30-0.36 error rate at best). We argued that our models performed best when handled using SMOTE
filter for imbalanced data.
The document describes latent Dirichlet allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora. LDA represents documents as random mixtures over latent topics, characterized by distributions over words. It is a three-level hierarchical Bayesian model where documents are generated by first sampling a per-document topic distribution from a Dirichlet prior, then repeatedly sampling topics and words from these distributions. LDA addresses limitations of previous models by capturing statistical structure within and between documents through the hierarchical Bayesian formulation.
Improvement of Text Summarization using Fuzzy Logic Based MethodIOSR Journals
The document describes a method for improving text summarization using fuzzy logic. It proposes using fuzzy logic to determine the importance of sentences based on calculated feature scores. Eight features are used to score sentences, including title words, length, term frequency, position, and similarity. Sentences are then ranked based on their fuzzy logic-determined scores. The highest scoring sentences are extracted to create a summary. An evaluation of summaries generated using this fuzzy logic method found it performed better than other summarizers in accurately reflecting the content and order of human-generated reference summaries. The method could be expanded to multi-document summarization and automatic selection of fuzzy rules based on input type.
Feature selection, optimization and clustering strategies of text documentsIJECEIAES
Clustering is one of the most researched areas of data mining applications in the contemporary literature. The need for efficient clustering is observed across wide sectors including consumer segmentation, categorization, shared filtering, document management, and indexing. The research of clustering task is to be performed prior to its adaptation in the text environment. Conventional approaches typically emphasized on the quantitative information where the selected features are numbers. Efforts also have been put forward for achieving efficient clustering in the context of categorical information where the selected features can assume nominal values. This manuscript presents an in-depth analysis of challenges of clustering in the text environment. Further, this paper also details prominent models proposed for clustering along with the pros and cons of each model. In addition, it also focuses on various latest developments in the clustering task in the social network and associated environments.
Semantics-based clustering approach for similar research area detectionTELKOMNIKA JOURNAL
The manual process of searching out individuals in an already existing
research field is cumbersome and time-consuming. Prominent and rookie
researchers alike are predisposed to seek existing research publications in
a research field of interest before coming up with a thesis. From
extant literature, automated similar research area detection systems have
been developed to solve this problem. However, most of them use
keyword-matching techniques, which do not sufficiently capture the implicit
semantics of keywords thereby leaving out some research articles. In this
study, we propose the use of ontology-based pre-processing, Latent Semantic
Indexing and K-Means Clustering to develop a prototype similar research area
detection system, that can be used to determine similar research domain
publications. Our proposed system solves the challenge of high dimensionality
and data sparsity faced by the traditional document clustering technique. Our
system is evaluated with randomly selected publications from faculties
in Nigerian universities and results show that the integration of ontologies
in preprocessing provides more accurate clustering results.
This document discusses hierarchical clustering and similarity measures for document clustering. It summarizes that hierarchical clustering creates a hierarchical decomposition of data objects through either agglomerative or divisive approaches. The success of clustering depends on the similarity measure used, with traditional measures using a single viewpoint, while multiviewpoint measures use different viewpoints to increase accuracy. The paper then focuses on applying a multiviewpoint similarity measure to hierarchical clustering of documents.
This document summarizes a research paper on applying a multiviewpoint-based similarity measure to hierarchical document clustering. It begins by introducing document clustering and hierarchical clustering. It then discusses traditional similarity measures used for clustering and introduces a new multiviewpoint-based similarity measure (MVS) that uses multiple reference points to more accurately assess similarity. The paper applies MVS to both hierarchical and k-means clustering algorithms and evaluates the accuracy, precision, and recall of the resulting clusters. It finds that hierarchical clustering with MVS achieves better performance than k-means clustering with MVS based on these evaluation metrics.
Scaling Down Dimensions and Feature Extraction in Document Repository Classif...ijdmtaiir
-In this study a comprehensive evaluation of two
supervised feature selection methods for dimensionality
reduction is performed - Latent Semantic Indexing (LSI) and
Principal Component Analysis (PCA). This is gauged against
unsupervised techniques like fuzzy feature clustering using
hard fuzzy C-means (FCM) . The main objective of the study is
to estimate the relative efficiency of two supervised techniques
against unsupervised fuzzy techniques while reducing the
feature space. It is found that clustering using FCM leads to
better accuracy in classifying documents in the face of
evolutionary algorithms like LSI and PCA. Results show that
the clustering of features improves the accuracy of document
classification
International Journal of Engineering and Science Invention (IJESI)inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Comparative analysis of c99 and topictiling text segmentation algorithmseSAT Journals
Abstract In this paper, the work done includes the extraction of information from image datasets which contain natural text. The difficulty level of segmenting natural text from an image is too high and so precision is the most important factor to be kept in mind. To minimize the error rates, error filtration technique is provided, as filtration is adopted while doing image segmentation basically text segmentation present in images. Furthermore, a comparative analysis of two different text segmentation algorithms namely C99 and TopicTiling on image documents is presented. To assess how well each algorithm works, each was applied on different datasets and results were compared. The work done also proves the efficiency of TopicTiling over C99. Index Terms: Text Segmentation, text extraction, image documents,C99 and TopicTiling.
The document presents a comparative analysis of two text segmentation algorithms, C99 and TopicTiling, that are applied to extract natural text from image documents. It first discusses related work on text segmentation techniques. It then provides an overview of the two-phase implementation: 1) text is extracted from images using preprocessing, thresholding, boundary detection and text recognition, and 2) the extracted text is segmented using C99 and TopicTiling, and the results of each are compared. The analysis shows that TopicTiling performs more efficiently than C99 at segmenting text from images.
This summarizes an academic paper that proposes an automatic ontology creation method for classifying research papers. It uses text mining techniques like classification and clustering algorithms. It first builds a research ontology by extracting keywords and patterns from previous papers. It then uses a decision tree algorithm to classify new papers into disciplines defined in the ontology. The classified papers are then clustered based on similarities to group them. The method was tested on a dataset of 100 papers and achieved average precision of 85.7% for term-based and 89.3% for pattern-based keyword extraction.
A hybrid approach for text summarization using semantic latent Dirichlet allo...IJECEIAES
Automatic text summarization generates a summary that contains sentences reflecting the essential and relevant information of the original documents. Extractive summarization requires semantic understanding, while abstractive summarization requires a better intermediate text representation. This paper proposes a hybrid approach for generating text summaries that combine extractive and abstractive methods. To improve the semantic understanding of the model, we propose two novel extractive methods: semantic latent Dirichlet allocation (semantic LDA) and sentence concept mapping. We then generate an intermediate summary by applying our proposed sentence ranking algorithm over the sentence concept mapping. This intermediate summary is input to a transformer-based abstractive model fine-tuned with a multi-head attention mechanism. Our experimental results demonstrate that the proposed hybrid model generates coherent summaries using the intermediate extractive summary covering semantics. As we increase the concepts and number of words in the summary the rouge scores are improved for precision and F1 scores in our proposed model.
A Centroid And Relationship Based Clustering For Organizing Research PapersDaniel Wachtel
This document summarizes a research paper that proposes a new method for organizing research papers using centroid and relationship-based clustering. The method aims to group similar research papers together based on common terms in their titles, keywords, frequent sentences, and referenced titles. Only the most important information from each paper is considered, such as the title, keywords, top frequent sentences and most similar referenced papers, in order to reduce processing time for large collections of papers. The clustering algorithm uses these relationships between papers to assign them to topic clusters in an efficient and effective manner.
Keyword Extraction Based Summarization of Categorized Kannada Text Documents ijsc
The internet has caused a humongous growth in the number of documents available online. Summaries of documents can help find the right information and are particularly effective when the document base is very large. Keywords are closely associated to a document as they reflect the document's content and act as indices for a given document. In this work, we present a method to produce extractive summaries of documents in the Kannada language, given number of sentences as limitation. The algorithm extracts key words from pre-categorized Kannada documents collected from online resources. We use two feature selection techniques for obtaining features from documents, then we combine scores obtained by GSS (Galavotti, Sebastiani, Simi) coefficients and IDF (Inverse Document Frequency) methods along with TF (Term Frequency) for extracting key words and later use these for summarization based on rank of the sentence. In the current implementation, a document from a given category is selected from our database and depending on the number of sentences given by the user, a summary is generated.
EXPLOITING RHETORICAL RELATIONS TO MULTIPLE DOCUMENTS TEXT SUMMARIZATIONIJNSA Journal
Many of previous research have proven that the usage of rhetorical relations is capable to enhance many applications such as text summarization, question answering and natural language generation. This work proposes an approach that expands the benefit of rhetorical relations to address redundancy problem for cluster-based text summarization of multiple documents. We exploited rhetorical relations exist between sentences to group similar sentences into multiple clusters to identify themes of common information. The candidate summary were extracted from these clusters. Then, cluster-based text summarization is performed using Conditional Markov Random Walk Model to measure the saliency scores of the candidate summary. We evaluated our method by measuring the cohesion and separation of the clusters constructed by exploiting rhetorical relations and ROUGE score of generated summaries. The experimental result shows that our method performed well which shows promising potential of applying rhetorical relation in text clustering which benefits text summarization of multiple documents.
Single document keywords extraction in Bahasa Indonesia using phrase chunkingTELKOMNIKA JOURNAL
Keywords help readers to understand the idea of a document quickly. Unfortunately, considerable time and effort are often needed to come up with a good set of keywords manually. This research focused on generating keywords from a document automatically using phrase chunking. Firstly, we collected part of speech patterns from a collection of documents. Secondly, we used those patterns to extract candidate keywords from the abstract and the content of a document. Finally, keywords are selected from the candidates based on the number of words in the keyword phrases and some scenarios involving candidate reduction and sorting. We evaluated the result of each scenario using precision, recall, and F-measure. The experiment results show: i) shorter-phrase keywords with string reduction extracted from the abstract and sorted by frequency provides the highest score, ii) in every proposed scenario, extracting keywords using the abstract always presents a better result, iii) using shorter-phrase patterns in keywords extraction gives better score in comparison to using all phrase patterns, iv) sorting scenarios based on the multiplication of candidate frequencies and the weight of the phrase patterns offer better results.
Many of previous research have proven that the usage of rhetorical relations is capable to enhance many applications such as text summarization, question answering and natural language generation. This work proposes an approach that expands the benefit of rhetorical relations to address redundancy problem for cluster-based text summarization of multiple documents. We exploited rhetorical relations exist between sentences to group similar sentences into multiple clusters to identify themes of common information. The candidate summary were extracted from these clusters. Then, cluster-based text summarization is performed using Conditional Markov Random Walk Model to measure the saliency scores of the candidate summary. We evaluated our method by measuring the cohesion and separation of the clusters constructed by exploiting rhetorical relations and ROUGE score of generated summaries. The experimental result shows that our method performed well which shows promising potential of applying rhetorical relation in text clustering which benefits text summarization of multiple documents.
A SEMANTIC METADATA ENRICHMENT SOFTWARE ECOSYSTEM BASED ON TOPIC METADATA ENR...IJDKP
As existing computer search engines struggle to understand the meaning of natural language, semantically
enriched metadata may improve interest-based search engine capabilities and user satisfaction.
This paper presents an enhanced version of the ecosystem focusing on semantic topic metadata detection
and enrichments. It is based on a previous paper, a semantic metadata enrichment software ecosystem
(SMESE). Through text analysis approaches for topic detection and metadata enrichments this paper
propose an algorithm to enhance search engines capabilities and consequently help users finding content
according to their interests. It presents the design, implementation and evaluation of SATD (Scalable
Annotation-based Topic Detection) model and algorithm using metadata from the web, linked open data,
concordance rules, and bibliographic record authorities. It includes a prototype of a semantic engine using
keyword extraction, classification and concept extraction that allows generating semantic topics by text,
and multimedia document analysis using the proposed SATD model and algorithm.
The performance of the proposed ecosystem is evaluated using a number of prototype simulations by
comparing them to existing enriched metadata techniques (e.g., AlchemyAPI, DBpedia, Wikimeta, Bitext,
AIDA, TextRazor). It was noted that SATD algorithm supports more attributes than other algorithms. The
results show that the enhanced platform and its algorithm enable greater understanding of documents
related to user interests.
CONTEXT-AWARE CLUSTERING USING GLOVE AND K-MEANSijseajournal
ABSTRACT
In this paper we propose a novel method to cluster categorical data while retaining their context. Typically, clustering is performed on numerical data. However it is often useful to cluster categorical data as well, especially when dealing with data in real-world contexts. Several methods exist which can cluster categorical data, but our approach is unique in that we use recent text-processing and machine learning advancements like GloVe and t- SNE to develop a a context-aware clustering approach (using pre-trained
word embeddings). We encode words or categorical data into numerical, context-aware, vectors that we use to cluster the data points using common clustering algorithms like K-means.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
IRJET- Multi-Document Summarization using Fuzzy and Hierarchical ApproachIRJET Journal
This document discusses multi-document summarization using fuzzy and hierarchical approaches. It begins with an abstract describing multi-document summarization as extracting important information from multiple source documents to create a short summary. The introduction discusses the need for efficient multi-document summarization due to the large amount of online information. It then reviews related literature on multi-document summarization techniques including neuro-fuzzy approaches and modified K-nearest neighbor algorithms. Finally, it describes the proposed methodology which uses statistical approaches like similarity measures, page rank and expectation maximization to cluster sentences and extract a summary from the clustered sentences.
Similar to Complete agglomerative hierarchy document’s clustering based on fuzzy luhn’s gibbs latent dirichlet allocation (20)
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Neural network optimizer of proportional-integral-differential controller par...IJECEIAES
Wide application of proportional-integral-differential (PID)-regulator in industry requires constant improvement of methods of its parameters adjustment. The paper deals with the issues of optimization of PID-regulator parameters with the use of neural network technology methods. A methodology for choosing the architecture (structure) of neural network optimizer is proposed, which consists in determining the number of layers, the number of neurons in each layer, as well as the form and type of activation function. Algorithms of neural network training based on the application of the method of minimizing the mismatch between the regulated value and the target value are developed. The method of back propagation of gradients is proposed to select the optimal training rate of neurons of the neural network. The neural network optimizer, which is a superstructure of the linear PID controller, allows increasing the regulation accuracy from 0.23 to 0.09, thus reducing the power consumption from 65% to 53%. The results of the conducted experiments allow us to conclude that the created neural superstructure may well become a prototype of an automatic voltage regulator (AVR)-type industrial controller for tuning the parameters of the PID controller.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
A review on features and methods of potential fishing zoneIJECEIAES
This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.
Electrical signal interference minimization using appropriate core material f...IJECEIAES
As demand for smaller, quicker, and more powerful devices rises, Moore's law is strictly followed. The industry has worked hard to make little devices that boost productivity. The goal is to optimize device density. Scientists are reducing connection delays to improve circuit performance. This helped them understand three-dimensional integrated circuit (3D IC) concepts, which stack active devices and create vertical connections to diminish latency and lower interconnects. Electrical involvement is a big worry with 3D integrates circuits. Researchers have developed and tested through silicon via (TSV) and substrates to decrease electrical wave involvement. This study illustrates a novel noise coupling reduction method using several electrical involvement models. A 22% drop in electrical involvement from wave-carrying to victim TSVs introduces this new paradigm and improves system performance even at higher THz frequencies.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
efficient power tracking in photovoltaic systems.
Adaptive synchronous sliding control for a robot manipulator based on neural ...IJECEIAES
Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for
robot hands is always an attractive topic in the research community. This is a
challenging problem because robot manipulators are complex nonlinear systems
and are often subject to fluctuations in loads and external disturbances. This
article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller
ensures that the positions of the joints track the desired trajectory, synchronize
the errors, and significantly reduces chattering. First, the synchronous tracking
errors and synchronous sliding surfaces are presented. Second, the synchronous
tracking error dynamics are determined. Third, a robust adaptive control law is
designed,the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy
logic. The built algorithm ensures that the tracking and approximation errors
are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results.
Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is
significantly reduced.
Remote field-programmable gate array laboratory for signal acquisition and de...IJECEIAES
A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embeddedsystem design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.
Detecting and resolving feature envy through automated machine learning and m...IJECEIAES
Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.
Smart monitoring technique for solar cell systems using internet of things ba...IJECEIAES
Rapidly and remotely monitoring and receiving the solar cell systems status parameters, solar irradiance, temperature, and humidity, are critical issues in enhancement their efficiency. Hence, in the present article an improved smart prototype of internet of things (IoT) technique based on embedded system through NodeMCU ESP8266 (ESP-12E) was carried out experimentally. Three different regions at Egypt; Luxor, Cairo, and El-Beheira cities were chosen to study their solar irradiance profile, temperature, and humidity by the proposed IoT system. The monitoring data of solar irradiance, temperature, and humidity were live visualized directly by Ubidots through hypertext transfer protocol (HTTP) protocol. The measured solar power radiation in Luxor, Cairo, and El-Beheira ranged between 216-1000, 245-958, and 187-692 W/m 2 respectively during the solar day. The accuracy and rapidity of obtaining monitoring results using the proposed IoT system made it a strong candidate for application in monitoring solar cell systems. On the other hand, the obtained solar power radiation results of the three considered regions strongly candidate Luxor and Cairo as suitable places to build up a solar cells system station rather than El-Beheira.
An efficient security framework for intrusion detection and prevention in int...IJECEIAES
Over the past few years, the internet of things (IoT) has advanced to connect billions of smart devices to improve quality of life. However, anomalies or malicious intrusions pose several security loopholes, leading to performance degradation and threat to data security in IoT operations. Thereby, IoT security systems must keep an eye on and restrict unwanted events from occurring in the IoT network. Recently, various technical solutions based on machine learning (ML) models have been derived towards identifying and restricting unwanted events in IoT. However, most ML-based approaches are prone to miss-classification due to inappropriate feature selection. Additionally, most ML approaches applied to intrusion detection and prevention consider supervised learning, which requires a large amount of labeled data to be trained. Consequently, such complex datasets are impossible to source in a large network like IoT. To address this problem, this proposed study introduces an efficient learning mechanism to strengthen the IoT security aspects. The proposed algorithm incorporates supervised and unsupervised approaches to improve the learning models for intrusion detection and mitigation. Compared with the related works, the experimental outcome shows that the model performs well in a benchmark dataset. It accomplishes an improved detection accuracy of approximately 99.21%.
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Online train ticket booking system project.pdfKamal Acharya
Rail transport is one of the important modes of transport in India. Now a days we
see that there are railways that are present for the long as well as short distance
travelling which makes the life of the people easier. When compared to other
means of transport, a railway is the cheapest means of transport. The maintenance
of the railway database also plays a major role in the smooth running of this
system. The Online Train Ticket Management System will help in reserving the
tickets of the railways to travel from a particular source to the destination.
Cricket management system ptoject report.pdfKamal Acharya
The aim of this project is to provide the complete information of the National and
International statistics. The information is available country wise and player wise. By
entering the data of eachmatch, we can get all type of reports instantly, which will be
useful to call back history of each player. Also the team performance in each match can
be obtained. We can get a report on number of matches, wins and lost.
Covid Management System Project Report.pdfKamal Acharya
CoVID-19 sprang up in Wuhan China in November 2019 and was declared a pandemic by the in January 2020 World Health Organization (WHO). Like the Spanish flu of 1918 that claimed millions of lives, the COVID-19 has caused the demise of thousands with China, Italy, Spain, USA and India having the highest statistics on infection and mortality rates. Regardless of existing sophisticated technologies and medical science, the spread has continued to surge high. With this COVID-19 Management System, organizations can respond virtually to the COVID-19 pandemic and protect, educate and care for citizens in the community in a quick and effective manner. This comprehensive solution not only helps in containing the virus but also proactively empowers both citizens and care providers to minimize the spread of the virus through targeted strategies and education.
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as text, images, sound or video. For text-shaped data, the text is processed first through several steps such as
tokenization, filtering, lemmatization or stemming [12].
The results of text processing will be used to generate terms of indexing, which is a vocabulary
extracted in the collection of texts, and determine a weight for each term [13]. The terms and its weights will
be used to determine the distance between data to be merged in agglomerative algorithm. There are several
methods of term weighting in text processing [14]. For vector space model, there is a commonly used Term
Frequency-Inverse Document Frequency (TF-IDF) [15],[16]. To overcome the weakness of TF-IDF in
addressing synonym and polysemy in natural language, Latent Semantic Indexing (LSI) was developed.
Researches on text clustering with Agglomerative Hierarchical Clustering (AHC) algorithm has been done
with those term weighting schemes. The AHC algorithm with TF-IDF has been used to cluster the web pages
[17], construct taxonomies from a corpus of text documents [18], construct multi-keyword ranked search
scheme [19], context aware document clustering [20], automatic taxonomy construction from keywords [21].
The AHC algorithm has also been developed with LSI for document clustering [22], clustering of news
articles [23], information retrieval [24]. The weakness of LSI is overcome by developing a topic-based
weighting term called Latent Dirichlet allocation (LDA). LDA is a generative probabilistic model of a
corpus, which documents are represented as random mixtures over latent topics, and each topic is
characterized by a distribution over words [25]. A document in a corpus is not only identified as a single
topic, but can be identified as several topics with their respective probabilities [26]-[28].
LDA has been developed based on a hierarchy, known as hLDA [29], but this method is not able to
capture the hierarchical relationship that is formed [30]. Therefore, research needs to be done to classify
documents hierarchically by using hierarchical clustering method and LDA for weighting term. The research
that integrates LDA into the hierarchical clustering method, especially agglomerative, has already been done.
X. Li, H. Wang, G. Yin, T. Wang, C. Yang, Y. Yu, D. Tang [31] used LDA for inducing taxonomy from tags
based on word clustering. AHC framework is used to determine how similar every two tags, and then LDA is
used to capture thematic correlations among tags that resulted by AHC. D. Tu, L. Chen, G. Chen [32] used
LDA to extract the most typical words in every latent topic and apply a multi-way hierarchical agglomerative
clustering algorithm (AHC and WordNet) to cluster candidate concept words. The problem is those papers
discussed about English text. Until now, the performance of using the LDA method and agglomerative
hierarchical clustering in Indonesian text has never been published. If both of these methods are proven to
have good performance in clustering Indonesian texts, then it can also be used on other text mining tasks,
for example for document summarization.
To overcome this problem, in this research, AHC and LDA are used to cluster documents,
where LDA is not used for clustering, but used to generate the weight of terms contained in document text.
This research has differences with other related researches for Indonesian text. First, the term weighting
calculation used Luhn’s Idea to select the terms of text by defining upper cut-off and lower cut-off, and then
extracts the feature of terms using Gibbs Sampling LDA combined with the term frequency values and fuzzy
Sugeno logic. While, in other research, P.M. Prihatini, I.K.G.D. Putra, I.A.D. Giriantari, M. Sudarma [26]
used only TF-IDF for term weighting calculation. Second, the calculation of the distance between documents
for AHC is topic-based because it uses the value that resulted by Fuzzy Luhn’s Gibbs LDA.
Third, the document clustering with AHC uses three characteristics: single link, complete link and average
link based on Fuzzy Luhn’s Gibbs LDA, and then compares the best AHC characteristics with measurement
metrics. While, in other researches, Yuhefizar, B. Santosa, I.K. Eddy, Y.K. Suprapto [33] used Euclidean for
distance calculation and single linkage for document clustering. M.A.A. Riyadi, D.S. Pratiwi, A.R. Irawan,
K. Fithriasari [34] used single link, complete link and Ward’s link based on autocorrelation distance.
The discussion in this research is divided as follows. Section 2 discusses about research method. Section 3
discusses about the results and its analysis. Section 4 discusses about the conclusion of this research.
2. RESEARCH METHOD
This research consists of several steps, such as document text processing, term weighting with
Fuzzy Luhn’s Gibbs LDA, documents clustering with Fuzzy Luhn’s Gibbs LDA, and evaluation, as shown in
Figure 1.
2.1. Document text processing
In this research, the documents used are news text files obtained from Indonesian news website.
Each file is delimited into a collection of terms in the tokenization process. In the filtering process, each term
is filtered using a stop words list resulting in a meaningful set of terms. The terms generated by the filtering
process, some are already in the form of basic word, some are still have affixes. To make all terms in a
uniform shape, all terms are parsing into basic words through the stemming process. In this research,
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stemming uses the deletion of affixes method based on rules and basic dictionary. The stemming algorithm
used is a modification of Nazief-Adriani [35].
Figure 1. The research method design
2.2. Term weigthing with Fuzzy Luhn’s Gibbs LDA
In this research, term weighting is done through term selection and feature extraction. The term
selection is based on the concept of Luhn's Idea, where each term is calculated based on its relative frequency
against all terms in the document text [36]. Luhn describes the relationship between the occurrence
frequencies of a term (term frequency) with the importance of a term in the document. The term with
medium-frequency is more important than high or low frequency terms. Low frequency terms are included in
the lower cut-off, while high frequency terms are included in the upper cut-off. Medium-frequency terms can
be obtained by cutting the upper and lower cut-off. To eliminate terms in the upper cut-off can be done by
filtering terms based on stop words list. However, to eliminate terms in the lower cut-off, so far there has
been no research that can determine effective ways to determine the lower cut-off limits.
In this research, the elimination of terms in the upper cut-off limit is done twice. First, it done by
filtering of terms based on stop word list. Second, the filtering results are filtered again through stemming
proccess. For the elimination of terms in the lower cut-off limit is based on the stemming result, with
different percentage removal values for each text document, as in (1). This is based on the idea that each
document has different text lengths, so that no single constant value can be taken for all documents. Variable
lcod (lower cut-off document) refers to the lower-cut-off constant value for document d (in the form of a
positive integer). Variable fsd (false-stemming document) refers to the number of unsuccessful term
stemming in document d. Variable frd (filtering result document) refers to the number of terms in document d
used for the stemming process. Variable tsd (true-stemming document) refers to the number of successful
term stemming in document d.
𝑙𝑐𝑜 𝑑 = (
𝑓𝑠 𝑑
𝑓𝑟 𝑑
) |𝑡𝑠 𝑑| (1)
The term selection result is a collection of selected terms of each document that have important
meanings to be processed at the feature extraction. In this research, feature extraction is done by topic-based
LDA method. LDA has some reasoning algorithms, one of which is Gibbs Sampling that have proven
effective in conducting the topic sampling process [28]. In general, in the initialization process,
Gibbs Sampling assigns the topic of each term randomly using a multinomial random function. However,
the use of this function cannot represent the existence of each term in the topic. Therefore, in this research,
the determination of topic for each term in the initialization process is done based on the highest occurrence
frequency (tf) of the term in all topics, as in (2). Variable zt,k similar with k refers to the topic. Variable tft,k
refers to tf value of term t on topic k. To calculate the probability of each term in the sampling process used
the formula as in (3). Variable pt,k refers to probability value of sampling for term t on topic k. Variable nkw-1
refers to the value of the topic-term matrix by ignoring the current term value. Variable V is the unique
number of terms in all documents. Variable ndk-1 refers to value of the document-topic matrix by ignoring
the current term value. Variable β determine the mixing proportion of documents on the topics, while α
determines the mixture components of words on the topics [37]. Variable K is the number of topic.
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𝑧𝑡,𝑘 = 𝑘 ~ max(𝑡𝑓𝑡,𝑘) (2)
𝑝𝑡,𝑘 =
𝑛𝑘𝑤−1 𝛽
(∑ 𝑛𝑘𝑤−1−1)+(𝑉 𝛽)
𝑛𝑑𝑘−1 ∝
(∑ 𝑛𝑑𝑘−1−1)+(𝐾 ∝ )
(3)
In general, Gibbs Sampling in the LDA requires several times iterations for the sampling process
until it reaches convergent conditions. This takes time and high complexity. The addition of fuzzy
Tsukamoto logic into the sampling process can accelerate the achievement of convergent conditions with
good measurement values [26]. The fuzzy logic concept that used in that research will be improved through
this research by using Sugeno method to increase the accuracy value, considering the output of fuzzy logic
which needed for sampling is a constant value. In this research, the upper and lower limits for the fuzzy curve
are determined based on the tf value of each term. Fuzzification uses a triangular curve with the probability
value of the sampling result for each term p, as in (4). Variable u[t] refers to the degree of membership for
term t. Variable a refers to the lower bound of the curve. Variable b refers to the peak of the curve. Variable c
refers to the upper bound of the curve. The implication function used is OR because fuzzy logic here is used
to determine the probability value of term for one topic, while all topic will be determined in sampling
process. The rule composition generates the αp value based on the maximum value of all u[t] as in (5),
and the value of zo is based on the term probability value across the topic whose value is not equal to zero,
as in (6). Variable t refers to the term probability of sampling result. Variable zo refers to the composition
output. Variable n refers to the number of topics whose term probability is not equal to zero. For the
defuzzification, the final output of fuzzy z is obtained by calculating the mean value, as in (7). The value of z
is used as the probability value of term p for topic k and will be used for the next sampling process until it
reaches convergent conditions. After convergence, the final value of z will be the feature value for each term
and ready for clustering.
𝜇[𝑡] = {
0, 𝑡 ≤ 𝑎, 𝑡 ≥ 𝑐
𝑡−𝑎
𝑏−𝑎
, 𝑎 ≤ 𝑡 ≤ 𝑏
𝑏−𝑡
𝑐−𝑏
, 𝑏 ≤ 𝑡 ≤ 𝑐
(4)
∝ 𝑝= max(𝜇[𝑡1], 𝜇[𝑡2], … , 𝜇[𝑡 𝑘]) (5)
𝑧 𝑜 =
∑(𝑡≠0)
𝑛
(6)
𝑧 =
∝ 𝑝 𝑧 𝑜
∝ 𝑝
(7)
2.3. Documents clustering with Fuzzy Luhn’s Gibbs LDA
The feature values obtained at the feature extraction are used to calculate the distance between
documents to be used in the clustering process. In this research, distance calculations using the Cosine
Similarity, as in (8). Variable |di-dj| refers to the distance between documents i and j. Variable di refers to
document i, while dj refers to document j.
|𝑑𝑖 − 𝑑𝑗| =
𝑑 𝑖 𝑥 𝑑 𝑗
√𝑑 𝑖
2 𝑥 𝑑 𝑗
2
(8)
The distance between documents is used to cluster document using three types of AHC algorithms.
In the Single Link AHC algorithm, clusters are based on the smallest distance between pairs of two
documents, as in (9). In the Complete Link AHC algorithm, clusters are based on the largest distance
between pairs of two documents, as in (10). In the Average Link AHC algorithm, clusters are based on the
average distance between pairs of two documents, as in (11). Variable dij refers to the selected pair of
documents i and j.
𝑑𝑖𝑗 = min(|𝑑𝑖 − 𝑑1|, |𝑑𝑖 − 𝑑2|, … , |𝑑𝑖 − 𝑑𝑗|) (9)
𝑑𝑖𝑗 = max(|𝑑𝑖 − 𝑑1|, |𝑑𝑖 − 𝑑2|, … , |𝑑𝑖 − 𝑑𝑗|) (10)
𝑑𝑖𝑗 = avg(|𝑑𝑖 − 𝑑1|, |𝑑𝑖 − 𝑑2|, … , |𝑑𝑖 − 𝑑𝑗|) (11)
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2.4. Metrics evaluation
In this research, evaluation is done in two steps: evaluation of the feature extraction results and
evaluation of the clustering results. The text document used in this research has been classified into five
categories by Indonesian news media websites, so it can be used as gold standard for the evaluation process.
Evaluation of feature extraction results is done by comparing results with lower cut-off and without
lower cut-off. An evaluation was also performed to compare the feature extraction results between the Fuzzy
Gibbs LDA method [26] and Fuzzy Luhn's Gibbs LDA that used in this research. The evaluation was
performed using two measurement metrics. First, the perplexity is used to measure the ability of the Fuzzy
Luhn's Gibbs LDA feature extraction method to generalize the hidden data, as in (12) and (13) [25].
The smaller value of the perplexity indicates the better performance of the method. Variable 𝑃(𝑊̃ |𝑀) refers
to perplexity value. Variable M refers to the number of documents. Variable V is the unique number of terms
in all documents. Variable 𝑁 𝑚
𝑡
refers to the number of occurrences of the word t in document m. Variable K
is the number of topic. Variable 𝜑 𝑘,𝑡 refers to the number of documents for each topic. Variable 𝜗 𝑚,𝑘 refers to
the number of words for each topic.
𝑃(𝑊̃ |𝑀) = exp −
∑ log 𝑝(𝑤⃗⃗ 𝑚|𝑀)𝑀
𝑚=1
∑ 𝑁 𝑚
𝑀
𝑚=1
(12)
log 𝑝(𝑤⃗⃗ 𝑚|𝑀) = ∑ 𝑁 𝑚
𝑡𝑉
𝑡=1 log(∑ 𝜑 𝑘,𝑡
𝐾
𝑘=1 . 𝜗 𝑚,𝑘) (13)
Second, Precision (P), Recall (R) and F-Score (F) metrics are used to measure the ability of the
Fuzzy Luhn's Gibbs LDA feature extraction method in finding relevant documents according to the gold
standard, as in (14)-(16) [14]. Variable TP is true positive, refers to the number of relevant items retrieved.
Variable FP is false positive, refers to the number of non-relevant items retrieved. Variable FN is false
negative, refers to the number of relevant items that cannot retrieved. The greater value of P, R and F
indicates the better performance of the methods.
𝑃 =
𝑇𝑃
𝑇𝑃+𝐹𝑃
(14)
𝑅 =
𝑇𝑃
𝑇𝑃+𝐹𝑁
(15)
𝐹 =
2 . 𝑃 . 𝑅
𝑃+𝑅
(16)
Evaluation of clustering results is done by comparing the clustering results between Single Link,
Complete Link and Average Link AHC using feature extraction results. In this research, the evaluation was
performed using five measurement metrics. Precision, Recall, and F-Score (PRF) are used to measure the
ability of methods to cluster relevant documents according to the gold standard, as in (14)-(16). The fourth is
Normalized Mutual Information (NMI), as in (17)-(20) [38]. Variable 𝐼(Ω, 𝐶) refers to the value of mutual
information for class (gold standard) and cluster. Variable 𝐻(Ω) refers to the entropy of class. Variable 𝐻(𝐶)
refers to the entropy of cluster. Variable 𝑤 𝑘 refers to the number of document belongs to class k. Variable 𝑐𝑗
refers to the number of document belongs to cluster j.
𝑁𝑀𝐼 =
2 𝐼(Ω,𝐶)
𝐻(Ω)+𝐻(𝐶)
(17)
𝐼(Ω, 𝐶) = ∑ ∑
|𝑤 𝑘∩𝑐 𝑗|
𝑁𝑗𝑘 log
𝑁|𝑤 𝑘∩𝑐 𝑗|
|𝑤 𝑘||𝑐 𝑗|
(18)
𝐻(Ω) = − ∑
|𝑤 𝑘|
𝑁𝑘 log
|𝑤 𝑘|
𝑁
(19)
𝐻(𝐶) = − ∑
|𝑐 𝑗|
𝑁𝑗 log
|𝑐 𝑗|
𝑁
(20)
The fifth is Adjusted Rand Index (ARI), as in (21)-(24) [39]. Variable 𝑚𝑖𝑗 refers to the number of
document belongs to class i and cluster j. Variable 𝐶𝑖 refers to the number of document belongs to class i.
Variable 𝐶′𝑗 refers to the number of document belongs to cluster j. The greater value of P, R, F, NMI and
ARI indicates the better performance of the methods.
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𝐴𝑅𝐼 =
∑ ∑ (
𝑚 𝑖𝑗
2
)−𝑡3
𝑙
𝑗=1
𝑘
𝑖=1
1
2
(𝑡1+𝑡2)−𝑡3
(21)
𝑡1 = ∑ (|𝐶 𝑖|
2
)𝑘
𝑖=1 (22)
𝑡2 = ∑ (
|𝐶′ 𝑗|
2
)𝑙
𝑗=1 (23)
𝑡3 =
2𝑡1 𝑡2
𝑛(𝑛−1)
(24)
3. RESULTS AND ANALYSIS
3.1. Fuzzy Luhn’s Gibbs LDA
The evaluation results of the Fuzzy Luhn's Gibbs LDA feature extraction method can be seen in
Table 1. The value in the table shows the comparison between Fuzzy Luhn's Gibbs LDA feature extraction
method and Fuzzy Gibbs LDA that have published by P.M. Prihatini, I.K.G.D. Putra, I.A.D. Giriantari, M.
Sudarma [26]. The Fuzzy Luhn's Gibbs LDA feature extraction method uses lower cut-off and without lower
cut-off for the selection feature method, while Fuzzy Gibbs LDA did not use Luhn’s concept.
Table 1. Comparison of Fuzzy Luhn’s Gibbs LDA and Fuzzy Gibbs LDA
Metrics
evaluations
Fuzzy Luhn’s Gibbs LDA The difference Fuzzy Gibbs LDA The difference
Lower cut-off (1) Without lower cut-off (2) (1) & (2) (3) (1) & (3) (2) & (3)
Perplexity 0.0375 0.0339 0.0036 0.0376 0.0001 0.0037
Precision 0.9435 0.9515 0.0080 0.8975 0.0460 0.0540
Recall 0.9280 0.9360 0.0080 0.8486 0.0794 0.0874
F-Score 0.9296 0.9387 0.0091 0.8420 0.0876 0.0967
The evaluation results in Table 1 shows that the feature extraction with lower cut-off using equation
(1) gives the evaluation value not much different than without the lower cut-off. The difference of metric
measurement values between the two methods is very small with the range from 0.0036 to 0.0091. This
insignificant difference occurs because the feature selection in this research has been done through two step
of the upper cut-off, which at filtering step with stop word list and then at stemming step. These two steps
have filtered the term with frequencies that appear frequently and rarely appear, so it results a list of
meaningful terms for the feature extraction. The lower cut-off process with the value adjusted to the length of
the document only removes a small portion of the meaningful term in the feature selection so it does not
significantly affect the feature extraction results.
The evaluation results in Table 1 also shows Fuzzy Gibbs LDA method resulted perplexity of
0.0376, while Fuzzy Luhn's Gibbs LDA in this research gives the value of perplexity 0.0375 for lower cut-
off and 0.0339 without lower cut-off. This indicates that the Fuzzy Luhn's Gibbs LDA algorithm performs
as well as Fuzzy Gibbs LDA in generating hidden data. But, the results of the P, R, and F metric indicate that
the Fuzzy Luhn's Gibbs LDA algorithm performed gives better results ranging from 0.9280 to 0.9515 than
Fuzzy Gibbs LDA algorithm ranging from 0.8420 to 0.8975. The increasing value of PRF metric shows that
the topic determination of each term for initial sampling that performed based on the highest occurrence
frequency (tf) of term to all topics by using Luhn’s Idea and the use of the Fuzzy Sugeno method is better
able to find documents relevant to the gold standard. This indicates that Fuzzy Luhn's Gibbs LDA algorithm
is a better choice in performing feature extraction for clustering.
2.2. AHC with Fuzzy Luhn’s Gibbs LDA
The evaluation results of the AHC algorithms performed based on the Fuzzy Luhn's Gibbs LDA
feature extraction can be seen in Table 2. The evaluation results in Table 2 shows that the feature selections
with lower cut-off or without lower cut-off do not affect the performance of the AHC algorithms in the
clustering process. It can be seen from the measurement metric values that both feature selection methods
produce Complete Link AHC algorithm as the AHC clustering algorithm with the best metric value. The
differences for the Complete Link AHC algorithm with both feature selection methods ranges from 0.0003 to
0.0263. This shows that both feature selection methods can be used as a good choice in clustering process
with AHC. But, in terms of the consistency of the value generated by the five metric measurements,
Complete Link AHC and Fuzzy Luhn's Gibbs LDA with lower cut-off have consistent metric values,
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ranging from 0.9196 to 0.9989, with differences ranging from 0.0213 to 0.0793; while Complete Link AHC
and Fuzzy Luhn's Gibbs LDA without lower cut-off have values ranging from 0.8933 to 0.9974, with
differences ranging from 0.0213 to 0.0793, and decreased the NMI metric value 0.0263 compared to lower
cut-off. The results of AHC with Fuzzy Luhn’s Gibbs LDA compared with the results of AHC with
autocorrelation distance that have published by M.A.A. Riyadi, D.S. Pratiwi, A.R. Irawan, K. Fithriasari [34].
In their research, Complete Link AHC with Autocorrelation distance resulted accuracy value of 0.8235.
Therefore, the use of Complete Link AHC and Fuzzy Luhn's Gibbs LDA with lower cut-off is more relevant
as a better clustering method in clustering documents especially Indonesian text news.
Table 2. Evaluation Results of AHC with Fuzzy Luhn’s Gibbs LDA
Metrics
Evaluations
Fuzzy Luhn’s Gibbs LDA with Lower cut-off Fuzzy Luhn’s Gibbs LDA without Lower cut-off
The
difference
Single Link
AHC
Complete Link
AHC (1)
Average Link
AHC
Single Link
AHC
Complete Link
AHC (2)
Average Link
AHC
(1)(2)
Precision 0.8255 0.9549 0.9169 0.8642 0.9583 0.9340 0.0034
Recall 0.6021 0.9273 0.8179 0.6714 0.9247 0.8201 0.0026
F-Score 0.6963 0.9409 0.8646 0.7557 0.9412 0.8733 0.0003
NMI 0.5827 0.9196 0.7208 0.6075 0.8933 0.6474 0.0263
ARI 0.9523 0.9989 0.9128 0.9534 0.9974 0.9017 0.0015
4. CONCLUSION
Complete Link AHC and Fuzzy Luhn's Gibbs LDA with lower cut-off algorithm that has built in
this research can improve the quality of clusters generation for document clustering especially for Indonesian
text news. This is shown by the value of evaluation metrics, which are Precision, Recall, F-Score, Perplexity,
Normalized Mutual Information, and Adjusted Rand Index. The values of Precision, Recall and F-Score for
lower cut-off have less difference than without the lower cut-off, which means both methods can be used in
term selection process. The values of Perplexity, Precision, Recall and F-Score for Fuzzy Luhn's Gibbs LDA
algorithm was increased, which means it performed better than Fuzzy Gibbs LDA. The value of Precision,
Recall, F-Score, Perplexity, Normalized Mutual Information, and Adjusted Rand Index showed that the
Complete Link AHC and Fuzzy Luhn's Gibbs LDA algorithm as the best AHC clustering algorithm, with or
without lower cut-off. But, the Complete Link AHC algorithm and Fuzzy Luhn's Gibbs LDA with lower cut-
off produce more consistency value for the five metric measurements, which means is more relevant to its
gold standard.
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BIOGRAPHIES OF AUTHORS
Putu Manik Prihatini was born in Bali (Indonesia) on March 17, 1980. She is earned a bachelor's
degree of informatics engineering at Sekolah Tinggi Teknologi Telkom Bandung (Indonesia) in
2002, and her master's degree at Universitas Udayana (Indonesia) in 2012. She is a lecturer at
Politeknik Negeri Bali (Indonesia) since 2002 until now. Currently, she is being a doctoral student
of Ilmu Teknik at Universitas Udayana (Indonesia). Her research and interest is Text Mining,
Information Retrieval System, and Soft Computing. Putu Manik Prihatini, ST, MT. Email:
manikprihatini@pnb.ac.id
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I Ketut Gede Darma Putra was born in Bali (Indonesia) on April 24, 1974. He is earned a
bachelor's degree of informatics at Institut Teknologi Sepuluh November (ITS-Indonesia); masters
and doctoral degrees at Universitas Gadjah Mada (UGM-Indonesia). He is a lecturer at Universitas
Udayana (Indonesia) since 1999 until now. His research and interest is Data Mining and Image
Processing. Prof. Dr. I Ketut Gede Darma Putra, S.Kom., MT. Email: ikgdarmaputra@unud.ac.id
Ida Ayu Dwi Giriantari was born in Bali (Indonesia) on December 13, 1965. She is earned a
bachelor's degree of electrical engineering at Universitas Udayana (Indonesia); masters and
doctoral degrees at The University of New South Wales (Australia). She is a lecturer at Universitas
Udayana Indonesia since 1991 until now. Her research and interest is electric power system,
renewable energy technology and application, smart grid and control. Prof. Ir. Ida Ayu Dwi
Giriantari, M.Eng,Sc., PhD. Email: dayu.giriantari @unud.ac.id
Made Sudarma was born in Bali (Indonesia) on December 31, 1965. He is earned a bachelor's
degree of informatics at Institut Teknologi Sepuluh Nopember (ITS-Indonesia); master’s degree at
School of Information Technology and Engineering, Ottawa University (Canada) and doctoral
degrees at Universitas Udayana (Indonesia). He is a lecturer at Universitas Udayana Indonesia
since 1993 until now. His research and interest is Data Mining and Image Processing. Dr. Ir. Made
Sudarma, M.A.Sc. Email: msudarma@unud.ac.id