This document discusses several approaches for clustering textual documents, including:
1. TF-IDF, word embedding, and K-means clustering are proposed to automatically classify and organize documents.
2. Previous work on document clustering is reviewed, including partition-based techniques like K-means and K-medoids, hierarchical clustering, and approaches using semantic features, PSO optimization, and multi-view clustering.
3. Challenges of clustering large document collections at scale are discussed, along with potential solutions using frameworks like Hadoop.
USING ONTOLOGIES TO IMPROVE DOCUMENT CLASSIFICATION WITH TRANSDUCTIVE SUPPORT...IJDKP
Many applications of automatic document classification require learning accurately with little training
data. The semi-supervised classification technique uses labeled and unlabeled data for training. This
technique has shown to be effective in some cases; however, the use of unlabeled data is not always
beneficial.
On the other hand, the emergence of web technologies has originated the collaborative development of
ontologies. In this paper, we propose the use of ontologies in order to improve the accuracy and efficiency
of the semi-supervised document classification.
We used support vector machines, which is one of the most effective algorithms that have been studied for
text. Our algorithm enhances the performance of transductive support vector machines through the use of
ontologies. We report experimental results applying our algorithm to three different datasets. Our
experiments show an increment of accuracy of 4% on average and up to 20%, in comparison with the
traditional semi-supervised model.
SOURCE CODE RETRIEVAL USING SEQUENCE BASED SIMILARITYIJDKP
This document summarizes an approach to improve source code retrieval using structural information from source code. A lexical parser is developed to extract control statements and method identifiers from Java programs. A similarity measure is proposed that calculates the ratio of fully matching statements to partially matching statements in a sequence. Experiments show the retrieval model using this measure improves retrieval performance over other models by up to 90.9% relative to the number of retrieved methods.
This document discusses GCUBE indexing, which is a method for indexing and aggregating spatial/continuous values in a data warehouse. The key challenges addressed are defining and aggregating spatial/continuous values, and efficiently representing, indexing, updating and querying data that includes both categorical and continuous dimensions. The proposed GCUBE approach maps multi-dimensional data to a linear ordering using the Hilbert curve, and then constructs an index structure on the ordered data to enable efficient query processing. Empirical results show the GCUBE indexing offers significant performance advantages over alternative approaches.
With the rapid development in Geographic Information Systems (GISs) and their applications, more and
more geo-graphical databases have been developed by different vendors. However, data integration and
accessing is still a big problem for the development of GIS applications as no interoperability exists among
different spatial databases. In this paper we propose a unified approach for spatial data query. The paper
describes a framework for integrating information from repositories containing different vector data sets
formats and repositories containing raster datasets. The presented approach converts different vector data
formats into a single unified format (File Geo-Database “GDB”). In addition, we employ “metadata” to
support a wide range of users’ queries to retrieve relevant geographic information from heterogeneous and
distributed repositories. Such an employment enhances both query processing and performance.
Clustering the results of a search helps the user to overview the information returned. In this paper, we
look upon the clustering task as cataloguing the search results. By catalogue we mean a structured label
list that can help the user to realize the labels and search results. Labelling Cluster is crucial because
meaningless or confusing labels may mislead users to check wrong clusters for the query and lose extra
time. Additionally, labels should reflect the contents of documents within the cluster accurately. To be able
to label clusters effectively, a new cluster labelling method is introduced. More emphasis was given to
/produce comprehensible and accurate cluster labels in addition to the discovery of document clusters. We
also present a new metric that employs to assess the success of cluster labelling. We adopt a comparative
evaluation strategy to derive the relative performance of the proposed method with respect to the two
prominent search result clustering methods: Suffix Tree Clustering and Lingo.
we perform the experiments using the publicly available Datasets Ambient and ODP-239
A CONCEPTUAL METADATA FRAMEWORK FOR SPATIAL DATA WAREHOUSEIJDKP
Metadata represents the information about data to be stored in Data Warehouses. It is a mandatory
element of Data Warehouse to build an efficient Data Warehouse. Metadata helps in data integration,
lineage, data quality and populating transformed data into data warehouse. Spatial data warehouses are
based on spatial data mostly collected from Geographical Information Systems (GIS) and the transactional
systems that are specific to an application or enterprise. Metadata design and deployment is the most
critical phase in building of data warehouse where it is mandatory to bring the spatial information and
data modeling together. In this paper, we present a holistic metadata framework that drives metadata
creation for spatial data warehouse. Theoretically, the proposed metadata framework improves the
efficiency of accessing of data in response to frequent queries on SDWs. In other words, the proposed
framework decreases the response time of the query and accurate information is fetched from Data
Warehouse including the spatial information
New proximity estimate for incremental update of non uniformly distributed cl...IJDKP
The conventional clustering algorithms mine static databases and generate a set of patterns in the form of
clusters. Many real life databases keep growing incrementally. For such dynamic databases, the patterns
extracted from the original database become obsolete. Thus the conventional clustering algorithms are not
suitable for incremental databases due to lack of capability to modify the clustering results in accordance
with recent updates. In this paper, the author proposes a new incremental clustering algorithm called
CFICA(Cluster Feature-Based Incremental Clustering Approach for numerical data) to handle numerical
data and suggests a new proximity metric called Inverse Proximity Estimate (IPE) which considers the
proximity of a data point to a cluster representative as well as its proximity to a farthest point in its vicinity.
CFICA makes use of the proposed proximity metric to determine the membership of a data point into a
cluster.
The document summarizes research on multi-document summarization using EM clustering. It begins with an introduction to the topic and issues with existing techniques. It then proposes using Expectation-Maximization (EM) clustering to identify clusters, which improves over other methods by identifying latent semantic variables between sentences. The architecture involves preprocessing, EM clustering, mutual reinforcement ranking algorithms RARP and RDRP, summarization, and post-processing. Experimental results on DUC2007 data show EM clustering identifies more clusters and sentences than affinity propagation clustering. The technique aims to improve summarization accuracy by better capturing semantic relationships between sentences.
USING ONTOLOGIES TO IMPROVE DOCUMENT CLASSIFICATION WITH TRANSDUCTIVE SUPPORT...IJDKP
Many applications of automatic document classification require learning accurately with little training
data. The semi-supervised classification technique uses labeled and unlabeled data for training. This
technique has shown to be effective in some cases; however, the use of unlabeled data is not always
beneficial.
On the other hand, the emergence of web technologies has originated the collaborative development of
ontologies. In this paper, we propose the use of ontologies in order to improve the accuracy and efficiency
of the semi-supervised document classification.
We used support vector machines, which is one of the most effective algorithms that have been studied for
text. Our algorithm enhances the performance of transductive support vector machines through the use of
ontologies. We report experimental results applying our algorithm to three different datasets. Our
experiments show an increment of accuracy of 4% on average and up to 20%, in comparison with the
traditional semi-supervised model.
SOURCE CODE RETRIEVAL USING SEQUENCE BASED SIMILARITYIJDKP
This document summarizes an approach to improve source code retrieval using structural information from source code. A lexical parser is developed to extract control statements and method identifiers from Java programs. A similarity measure is proposed that calculates the ratio of fully matching statements to partially matching statements in a sequence. Experiments show the retrieval model using this measure improves retrieval performance over other models by up to 90.9% relative to the number of retrieved methods.
This document discusses GCUBE indexing, which is a method for indexing and aggregating spatial/continuous values in a data warehouse. The key challenges addressed are defining and aggregating spatial/continuous values, and efficiently representing, indexing, updating and querying data that includes both categorical and continuous dimensions. The proposed GCUBE approach maps multi-dimensional data to a linear ordering using the Hilbert curve, and then constructs an index structure on the ordered data to enable efficient query processing. Empirical results show the GCUBE indexing offers significant performance advantages over alternative approaches.
With the rapid development in Geographic Information Systems (GISs) and their applications, more and
more geo-graphical databases have been developed by different vendors. However, data integration and
accessing is still a big problem for the development of GIS applications as no interoperability exists among
different spatial databases. In this paper we propose a unified approach for spatial data query. The paper
describes a framework for integrating information from repositories containing different vector data sets
formats and repositories containing raster datasets. The presented approach converts different vector data
formats into a single unified format (File Geo-Database “GDB”). In addition, we employ “metadata” to
support a wide range of users’ queries to retrieve relevant geographic information from heterogeneous and
distributed repositories. Such an employment enhances both query processing and performance.
Clustering the results of a search helps the user to overview the information returned. In this paper, we
look upon the clustering task as cataloguing the search results. By catalogue we mean a structured label
list that can help the user to realize the labels and search results. Labelling Cluster is crucial because
meaningless or confusing labels may mislead users to check wrong clusters for the query and lose extra
time. Additionally, labels should reflect the contents of documents within the cluster accurately. To be able
to label clusters effectively, a new cluster labelling method is introduced. More emphasis was given to
/produce comprehensible and accurate cluster labels in addition to the discovery of document clusters. We
also present a new metric that employs to assess the success of cluster labelling. We adopt a comparative
evaluation strategy to derive the relative performance of the proposed method with respect to the two
prominent search result clustering methods: Suffix Tree Clustering and Lingo.
we perform the experiments using the publicly available Datasets Ambient and ODP-239
A CONCEPTUAL METADATA FRAMEWORK FOR SPATIAL DATA WAREHOUSEIJDKP
Metadata represents the information about data to be stored in Data Warehouses. It is a mandatory
element of Data Warehouse to build an efficient Data Warehouse. Metadata helps in data integration,
lineage, data quality and populating transformed data into data warehouse. Spatial data warehouses are
based on spatial data mostly collected from Geographical Information Systems (GIS) and the transactional
systems that are specific to an application or enterprise. Metadata design and deployment is the most
critical phase in building of data warehouse where it is mandatory to bring the spatial information and
data modeling together. In this paper, we present a holistic metadata framework that drives metadata
creation for spatial data warehouse. Theoretically, the proposed metadata framework improves the
efficiency of accessing of data in response to frequent queries on SDWs. In other words, the proposed
framework decreases the response time of the query and accurate information is fetched from Data
Warehouse including the spatial information
New proximity estimate for incremental update of non uniformly distributed cl...IJDKP
The conventional clustering algorithms mine static databases and generate a set of patterns in the form of
clusters. Many real life databases keep growing incrementally. For such dynamic databases, the patterns
extracted from the original database become obsolete. Thus the conventional clustering algorithms are not
suitable for incremental databases due to lack of capability to modify the clustering results in accordance
with recent updates. In this paper, the author proposes a new incremental clustering algorithm called
CFICA(Cluster Feature-Based Incremental Clustering Approach for numerical data) to handle numerical
data and suggests a new proximity metric called Inverse Proximity Estimate (IPE) which considers the
proximity of a data point to a cluster representative as well as its proximity to a farthest point in its vicinity.
CFICA makes use of the proposed proximity metric to determine the membership of a data point into a
cluster.
The document summarizes research on multi-document summarization using EM clustering. It begins with an introduction to the topic and issues with existing techniques. It then proposes using Expectation-Maximization (EM) clustering to identify clusters, which improves over other methods by identifying latent semantic variables between sentences. The architecture involves preprocessing, EM clustering, mutual reinforcement ranking algorithms RARP and RDRP, summarization, and post-processing. Experimental results on DUC2007 data show EM clustering identifies more clusters and sentences than affinity propagation clustering. The technique aims to improve summarization accuracy by better capturing semantic relationships between sentences.
IRJET- Text Document Clustering using K-Means Algorithm IRJET Journal
This document discusses using the K-Means clustering algorithm to cluster text documents and compares it to using K-Means clustering with dimension reduction techniques. It uses the BBC Sports dataset containing 737 documents in 5 classes. The document outlines preprocessing the text, creating a document term matrix, applying K-Means clustering, and using dimension reduction techniques like InfoGain before clustering. It evaluates the different methods using precision, recall, accuracy, and F-measure, finding that K-Means with InfoGain dimension reduction outperforms standard K-Means clustering.
FAST FUZZY FEATURE CLUSTERING FOR TEXT CLASSIFICATION cscpconf
Feature clustering is a powerful method to reduce the dimensionality of feature vectors for text
classification. In this paper, Fast Fuzzy Feature clustering for text classification is proposed. It
is based on the framework proposed by Jung-Yi Jiang, Ren-Jia Liou and Shie-Jue Lee in 2011.
The word in the feature vector of the document is grouped into the cluster in less iteration. The
numbers of iterations required to obtain cluster centers are reduced by transforming clusters
center dimension from n-dimension to 2-dimension. Principle Component Analysis with slit
change is used for dimension reduction. Experimental results show that, this method improve
the performance by significantly reducing the number of iterations required to obtain the cluster
center. The same is being verified with three benchmark datasets
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.
This document summarizes various techniques for scalable continual top-k keyword search in relational databases. There are two main approaches: schema-based and graph-based. Schema-based methods generate candidate networks from the database schema and evaluate them. Graph-based methods represent the database as a graph and use techniques like bidirectional expansion. Top-k keyword search finds the highest scoring k results instead of all results. Methods like the Global Pipeline algorithm and Skyline-Sweeping algorithm efficiently process top-k queries over multiple candidate networks. Techniques for updating results with database changes include maintaining an initial top-k and recalculating scores. Lattice-based methods share computational costs for keyword search in data streams.
Survey on scalable continual top k keyword search in relational databaseseSAT Journals
Abstract Keyword search in relational database is a technique that has higher relevance in the present world. Extracting data from a large number of sets of database is very important .Because it reduces the usage of man power and time consumption. Data extraction from a large database using the relevant keyword based on the information needed is a very interactive and user friendly. Without knowing any database schemas or query languages like sql the user can get information. By using keyword in relational database data extraction will be simpler. The user doesn’t want to know the query language for search. But the database content is always changing for real time application for example database which store the data of publication data. When new publications arrive it should be added to database so the database content changes according to time. Because the database is updated frequently the result should change. In order to handle the database updation takes the top-k result from the currently updated data for each search. Top-k keyword search means take greatest k results based on the relevance of document. Keyword search in relational database means to find structural information from tuples from the database. Two types of keyword search are schema-based method and graph based approach. Using top-k keyword search instead of executing all query results taking highest k queries. By handling database updation try to find the new results and remove expired one
Textual Data Partitioning with Relationship and Discriminative AnalysisEditor IJMTER
Data partitioning methods are used to partition the data values with similarity. Similarity
measures are used to estimate transaction relationships. Hierarchical clustering model produces tree
structured results. Partitioned clustering produces results in grid format. Text documents are
unstructured data values with high dimensional attributes. Document clustering group ups unlabeled text
documents into meaningful clusters. Traditional clustering methods require cluster count (K) for the
document grouping process. Clustering accuracy degrades drastically with reference to the unsuitable
cluster count.
Textual data elements are divided into two types’ discriminative words and nondiscriminative
words. Only discriminative words are useful for grouping documents. The involvement of
nondiscriminative words confuses the clustering process and leads to poor clustering solution in return.
A variation inference algorithm is used to infer the document collection structure and partition of
document words at the same time. Dirichlet Process Mixture (DPM) model is used to partition
documents. DPM clustering model uses both the data likelihood and the clustering property of the
Dirichlet Process (DP). Dirichlet Process Mixture Model for Feature Partition (DPMFP) is used to
discover the latent cluster structure based on the DPM model. DPMFP clustering is performed without
requiring the number of clusters as input.
Document labels are used to estimate the discriminative word identification process. Concept
relationships are analyzed with Ontology support. Semantic weight model is used for the document
similarity analysis. The system improves the scalability with the support of labels and concept relations
for dimensionality reduction process.
With the development of database, the data volume stored in database increases rapidly and in the large
amounts of data much important information is hidden. If the information can be extracted from the
database they will create a lot of profit for the organization. The question they are asking is how to extract
this value. The answer is data mining. There are many technologies available to data mining practitioners,
including Artificial Neural Networks, Genetics, Fuzzy logic and Decision Trees. Many practitioners are
wary of Neural Networks due to their black box nature, even though they have proven themselves in many
situations. This paper is an overview of artificial neural networks and questions their position as a
preferred tool by data mining practitioners.
1) The document discusses a review of semantic approaches for nearest neighbor search. It describes using an ontology to add a semantic layer to an information retrieval system to relate concepts using query words.
2) A technique called spatial inverted index is proposed to locate multidimensional information and handle nearest neighbor queries by finding the hospitals closest to a given address.
3) Several semantic approaches are described including using clustering measures, specificity measures, link analysis, and relation-based page ranking to improve search and interpret hidden concepts behind keywords.
IRJET- Semantics based Document ClusteringIRJET Journal
This document describes a proposed ontology-based document clustering system. The system uses a two-step clustering algorithm that first applies K-means partitioning clustering followed by hierarchical agglomerative clustering. Ontology is introduced through a weighting scheme that integrates traditional TF-IDF word weights with weights of semantic relations between words from the ontology. The goal is to produce document clusters that are semantically meaningful by accounting for relationships between words, rather than just word co-occurrence. An overview of the system architecture and modules is provided, along with descriptions of preprocessing, concept weighting, clustering approaches, and initial implementation results.
This document presents a feature clustering algorithm to reduce the dimensionality of feature vectors for text classification. The algorithm groups words in documents into clusters based on similarity, with each cluster characterized by a membership function. Words not similar to existing clusters form new clusters. This avoids specifying features in advance and the need for trial and error. Experimental results showed the method can classify text faster and with better extracted features than other methods.
Feature Subset Selection for High Dimensional Data Using Clustering TechniquesIRJET Journal
The document discusses feature subset selection for high dimensional data using clustering techniques. It proposes the FAST algorithm which has three steps: 1) remove irrelevant features, 2) divide features into clusters using DBSCAN, and 3) select the most representative feature from each cluster. DBSCAN is a density-based clustering algorithm that can identify clusters of varying densities and detect outliers. The FAST algorithm is evaluated to select a small number of discriminative features from high dimensional data in an efficient manner. It aims to remove irrelevant and redundant features to improve predictive accuracy while handling large feature sets.
This document presents an approach for clustering a mixed dataset containing both numeric and categorical attributes using an ART-2 neural network model. The dataset contains daily stock price data with 19 attributes describing comparisons between consecutive days. Clustering mixed datasets is challenging due to different attribute types. The ART-2 model is used to classify the dataset without requiring a distance function. Then an autoencoder model reduces the dimensionality to allow visual validation of the clusters. The results demonstrate the ART-2 model's ability to cluster complex, mixed datasets.
TEXT SENTIMENTS FOR FORUMS HOTSPOT DETECTIONijistjournal
The user generated content on the web grows rapidly in this emergent information age. The evolutionary changes in technology make use of such information to capture only the user’s essence and finally the useful information are exposed to information seekers. Most of the existing research on text information processing, focuses in the factual domain rather than the opinion domain. In this paper we detect online hotspot forums by computing sentiment analysis for text data available in each forum. This approach analyses the forum text data and computes value for each word of text. The proposed approach combines K-means clustering and Support Vector Machine with PSO (SVM-PSO) classification algorithm that can be used to group the forums into two clusters forming hotspot forums and non-hotspot forums within the current time span. The proposed system accuracy is compared with the other classification algorithms such as Naïve Bayes, Decision tree and SVM. The experiment helps to identify that K-means and SVM-PSO together achieve highly consistent results.
Biometric retrieval is a challenging task as the size of the databases have increased considerably. In this work, a novel optimized kd-tree algorithm is implemented to enhance the efficiency of indexing and retrieving for a multibiometric database comprising of iris and fingerprints. To improve the retrieval performance, fingerprint image is represented by minutiae features and iris image is represented by texture features and the features are fused together by feature level fusion. Dimension reduction of the feature vector is carried out using Principal Component Analysis to reduce the storage space and increase retrieval rate. The proposed optimized kd-tree indexing technique with dimension reduction aims to overcome the limitations of the existing nearest kd-tree. From the experimental results, it is concluded that the proposed optimized kd-tree indexing algorithm with dimension reduction has reduced False Acceptance Rate and False Rejection Rate and has improved Hit rate to 95% at 60% penetration rate compared to existing nearest kd-tree techniquefor a multibiometric database.
Modeling Text Independent Speaker Identification with Vector QuantizationTELKOMNIKA JOURNAL
Speaker identification is one of the most important technologies nowadays. Many fields such as
bioinformatics and security are using speaker identification. Also, almost all electronic devices are using
this technology too. Based on number of text, speaker identification divided into text dependent and text
independent. On many fields, text independent is mostly used because number of text is unlimited. So, text
independent is generally more challenging than text dependent. In this research, speaker identification text
independent with Indonesian speaker data was modelled with Vector Quantization (VQ). In this research
VQ with K-Means initialization was used. K-Means clustering also was used to initialize mean and
Hierarchical Agglomerative Clustering was used to identify K value for VQ. The best VQ accuracy was
59.67% when k was 5. According to the result, Indonesian language could be modelled by VQ. This
research can be developed using optimization method for VQ parameters such as Genetic Algorithm or
Particle Swarm Optimization.
This document provides an overview of different techniques for clustering categorical data. It discusses various clustering algorithms that have been used for categorical data, including K-modes, ROCK, COBWEB, and EM algorithms. It also reviews more recently developed algorithms for categorical data clustering, such as algorithms based on particle swarm optimization, rough set theory, and feature weighting schemes. The document concludes that clustering categorical data remains an important area of research, with opportunities to develop techniques that initialize cluster centers better.
A Competent and Empirical Model of Distributed ClusteringIRJET Journal
This document discusses distributed document clustering. It begins with an introduction to how documents are stored and indexed in computers. It then discusses different clustering algorithms like hierarchical and k-means clustering that are used to group similar documents. The document proposes a new framework for efficiently clustering text documents stored across different distributed resources. It argues that traditional clustering algorithms cannot perfectly cluster text data in decentralized systems. The framework uses properties of traditional algorithms with the ability to cluster in distributed systems.
Iaetsd a survey on one class clusteringIaetsd Iaetsd
This document presents a new method for performing one-to-many data linkage called the One Class Clustering Tree (OCCT). The OCCT builds a tree structure with inner nodes representing features of the first dataset and leaves representing similar features of the second dataset. It uses splitting criteria and pruning methods to perform the data linkage more accurately than existing indexing techniques. The OCCT approach induces a decision tree using a splitting criteria and performs prepruning to determine which branches to trim. It then compares entities to match them between the two datasets and produces a final result.
IDENTIFICATION AND INVESTIGATION OF THE USER SESSION FOR LAN CONNECTIVITY VIA...ijcseit
This paper mainly presents some technical discussions on the identification and analyze of “LAN usersessions”.
The identification of a user-session is non trivial. Classical methods approaches rely on
threshold based mechanisms. Threshold based techniques are very sensitive to the value chosen for the
threshold, which may be difficult to set correctly. Clustering techniques are used to define a novel
methodology to identify LAN user-sessions without requiring an a priori definition of threshold values. We
have defined a clustering based approach in detail, and also we discussed positive and negative of this
approach, and we apply it to real traffic traces. The proposed methodology is applied to artificially
generated traces to evaluate its benefits against traditional threshold based approaches. We also analyzed
the characteristics of user-sessions extracted by the clustering methodology from real traces and study
their statistical properties.
Different Similarity Measures for Text Classification Using KnnIOSR Journals
This document summarizes research on classifying textual data using the k-nearest neighbors (KNN) algorithm and different similarity measures. It explores generating 9 different vector representations of text documents and using KNN with similarity measures like Euclidean, Manhattan, squared Euclidean, etc. to classify documents. The researchers tested KNN on a Reuters news corpus with 5,485 training documents across 8 classes and found that normalization and k=4 produced the best accuracy of 94.47%. They conclude KNN with different similarity measures and vector representations is effective for multi-class text classification.
Density Based Clustering Approach for Solving the Software Component Restruct...IRJET Journal
This document presents research on using the DBSCAN clustering algorithm to solve the problem of software component restructuring. It begins with an abstract that introduces DBSCAN and describes how it can group related software components. It then provides background on software component clustering and describes DBSCAN in more detail. The methodology section outlines the 4 phases of the proposed approach: data collection and processing, clustering with DBSCAN, visualization and analysis, and final restructuring. Experimental results show that DBSCAN produces more evenly distributed clusters compared to fuzzy clustering. The conclusion is that DBSCAN is a better technique for software restructuring as it can identify clusters of varying shapes and sizes without specifying the number of clusters in advance.
Reviews on swarm intelligence algorithms for text document clusteringIRJET Journal
This document reviews swarm intelligence algorithms that have been used for text document clustering. It discusses how text clustering is an unsupervised learning technique that groups similar documents into clusters while separating dissimilar documents. Various swarm intelligence algorithms like particle swarm optimization, artificial bee colony, grey wolf optimizer, and krill herd have been applied to text document clustering problems. The document surveys previous research that has used these swarm intelligence algorithms for text clustering and discusses their advantages and limitations. It aims to provide readers an overview of the different swarm intelligence algorithms available for text document clustering applications.
IRJET- Text Document Clustering using K-Means Algorithm IRJET Journal
This document discusses using the K-Means clustering algorithm to cluster text documents and compares it to using K-Means clustering with dimension reduction techniques. It uses the BBC Sports dataset containing 737 documents in 5 classes. The document outlines preprocessing the text, creating a document term matrix, applying K-Means clustering, and using dimension reduction techniques like InfoGain before clustering. It evaluates the different methods using precision, recall, accuracy, and F-measure, finding that K-Means with InfoGain dimension reduction outperforms standard K-Means clustering.
FAST FUZZY FEATURE CLUSTERING FOR TEXT CLASSIFICATION cscpconf
Feature clustering is a powerful method to reduce the dimensionality of feature vectors for text
classification. In this paper, Fast Fuzzy Feature clustering for text classification is proposed. It
is based on the framework proposed by Jung-Yi Jiang, Ren-Jia Liou and Shie-Jue Lee in 2011.
The word in the feature vector of the document is grouped into the cluster in less iteration. The
numbers of iterations required to obtain cluster centers are reduced by transforming clusters
center dimension from n-dimension to 2-dimension. Principle Component Analysis with slit
change is used for dimension reduction. Experimental results show that, this method improve
the performance by significantly reducing the number of iterations required to obtain the cluster
center. The same is being verified with three benchmark datasets
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.
This document summarizes various techniques for scalable continual top-k keyword search in relational databases. There are two main approaches: schema-based and graph-based. Schema-based methods generate candidate networks from the database schema and evaluate them. Graph-based methods represent the database as a graph and use techniques like bidirectional expansion. Top-k keyword search finds the highest scoring k results instead of all results. Methods like the Global Pipeline algorithm and Skyline-Sweeping algorithm efficiently process top-k queries over multiple candidate networks. Techniques for updating results with database changes include maintaining an initial top-k and recalculating scores. Lattice-based methods share computational costs for keyword search in data streams.
Survey on scalable continual top k keyword search in relational databaseseSAT Journals
Abstract Keyword search in relational database is a technique that has higher relevance in the present world. Extracting data from a large number of sets of database is very important .Because it reduces the usage of man power and time consumption. Data extraction from a large database using the relevant keyword based on the information needed is a very interactive and user friendly. Without knowing any database schemas or query languages like sql the user can get information. By using keyword in relational database data extraction will be simpler. The user doesn’t want to know the query language for search. But the database content is always changing for real time application for example database which store the data of publication data. When new publications arrive it should be added to database so the database content changes according to time. Because the database is updated frequently the result should change. In order to handle the database updation takes the top-k result from the currently updated data for each search. Top-k keyword search means take greatest k results based on the relevance of document. Keyword search in relational database means to find structural information from tuples from the database. Two types of keyword search are schema-based method and graph based approach. Using top-k keyword search instead of executing all query results taking highest k queries. By handling database updation try to find the new results and remove expired one
Textual Data Partitioning with Relationship and Discriminative AnalysisEditor IJMTER
Data partitioning methods are used to partition the data values with similarity. Similarity
measures are used to estimate transaction relationships. Hierarchical clustering model produces tree
structured results. Partitioned clustering produces results in grid format. Text documents are
unstructured data values with high dimensional attributes. Document clustering group ups unlabeled text
documents into meaningful clusters. Traditional clustering methods require cluster count (K) for the
document grouping process. Clustering accuracy degrades drastically with reference to the unsuitable
cluster count.
Textual data elements are divided into two types’ discriminative words and nondiscriminative
words. Only discriminative words are useful for grouping documents. The involvement of
nondiscriminative words confuses the clustering process and leads to poor clustering solution in return.
A variation inference algorithm is used to infer the document collection structure and partition of
document words at the same time. Dirichlet Process Mixture (DPM) model is used to partition
documents. DPM clustering model uses both the data likelihood and the clustering property of the
Dirichlet Process (DP). Dirichlet Process Mixture Model for Feature Partition (DPMFP) is used to
discover the latent cluster structure based on the DPM model. DPMFP clustering is performed without
requiring the number of clusters as input.
Document labels are used to estimate the discriminative word identification process. Concept
relationships are analyzed with Ontology support. Semantic weight model is used for the document
similarity analysis. The system improves the scalability with the support of labels and concept relations
for dimensionality reduction process.
With the development of database, the data volume stored in database increases rapidly and in the large
amounts of data much important information is hidden. If the information can be extracted from the
database they will create a lot of profit for the organization. The question they are asking is how to extract
this value. The answer is data mining. There are many technologies available to data mining practitioners,
including Artificial Neural Networks, Genetics, Fuzzy logic and Decision Trees. Many practitioners are
wary of Neural Networks due to their black box nature, even though they have proven themselves in many
situations. This paper is an overview of artificial neural networks and questions their position as a
preferred tool by data mining practitioners.
1) The document discusses a review of semantic approaches for nearest neighbor search. It describes using an ontology to add a semantic layer to an information retrieval system to relate concepts using query words.
2) A technique called spatial inverted index is proposed to locate multidimensional information and handle nearest neighbor queries by finding the hospitals closest to a given address.
3) Several semantic approaches are described including using clustering measures, specificity measures, link analysis, and relation-based page ranking to improve search and interpret hidden concepts behind keywords.
IRJET- Semantics based Document ClusteringIRJET Journal
This document describes a proposed ontology-based document clustering system. The system uses a two-step clustering algorithm that first applies K-means partitioning clustering followed by hierarchical agglomerative clustering. Ontology is introduced through a weighting scheme that integrates traditional TF-IDF word weights with weights of semantic relations between words from the ontology. The goal is to produce document clusters that are semantically meaningful by accounting for relationships between words, rather than just word co-occurrence. An overview of the system architecture and modules is provided, along with descriptions of preprocessing, concept weighting, clustering approaches, and initial implementation results.
This document presents a feature clustering algorithm to reduce the dimensionality of feature vectors for text classification. The algorithm groups words in documents into clusters based on similarity, with each cluster characterized by a membership function. Words not similar to existing clusters form new clusters. This avoids specifying features in advance and the need for trial and error. Experimental results showed the method can classify text faster and with better extracted features than other methods.
Feature Subset Selection for High Dimensional Data Using Clustering TechniquesIRJET Journal
The document discusses feature subset selection for high dimensional data using clustering techniques. It proposes the FAST algorithm which has three steps: 1) remove irrelevant features, 2) divide features into clusters using DBSCAN, and 3) select the most representative feature from each cluster. DBSCAN is a density-based clustering algorithm that can identify clusters of varying densities and detect outliers. The FAST algorithm is evaluated to select a small number of discriminative features from high dimensional data in an efficient manner. It aims to remove irrelevant and redundant features to improve predictive accuracy while handling large feature sets.
This document presents an approach for clustering a mixed dataset containing both numeric and categorical attributes using an ART-2 neural network model. The dataset contains daily stock price data with 19 attributes describing comparisons between consecutive days. Clustering mixed datasets is challenging due to different attribute types. The ART-2 model is used to classify the dataset without requiring a distance function. Then an autoencoder model reduces the dimensionality to allow visual validation of the clusters. The results demonstrate the ART-2 model's ability to cluster complex, mixed datasets.
TEXT SENTIMENTS FOR FORUMS HOTSPOT DETECTIONijistjournal
The user generated content on the web grows rapidly in this emergent information age. The evolutionary changes in technology make use of such information to capture only the user’s essence and finally the useful information are exposed to information seekers. Most of the existing research on text information processing, focuses in the factual domain rather than the opinion domain. In this paper we detect online hotspot forums by computing sentiment analysis for text data available in each forum. This approach analyses the forum text data and computes value for each word of text. The proposed approach combines K-means clustering and Support Vector Machine with PSO (SVM-PSO) classification algorithm that can be used to group the forums into two clusters forming hotspot forums and non-hotspot forums within the current time span. The proposed system accuracy is compared with the other classification algorithms such as Naïve Bayes, Decision tree and SVM. The experiment helps to identify that K-means and SVM-PSO together achieve highly consistent results.
Biometric retrieval is a challenging task as the size of the databases have increased considerably. In this work, a novel optimized kd-tree algorithm is implemented to enhance the efficiency of indexing and retrieving for a multibiometric database comprising of iris and fingerprints. To improve the retrieval performance, fingerprint image is represented by minutiae features and iris image is represented by texture features and the features are fused together by feature level fusion. Dimension reduction of the feature vector is carried out using Principal Component Analysis to reduce the storage space and increase retrieval rate. The proposed optimized kd-tree indexing technique with dimension reduction aims to overcome the limitations of the existing nearest kd-tree. From the experimental results, it is concluded that the proposed optimized kd-tree indexing algorithm with dimension reduction has reduced False Acceptance Rate and False Rejection Rate and has improved Hit rate to 95% at 60% penetration rate compared to existing nearest kd-tree techniquefor a multibiometric database.
Modeling Text Independent Speaker Identification with Vector QuantizationTELKOMNIKA JOURNAL
Speaker identification is one of the most important technologies nowadays. Many fields such as
bioinformatics and security are using speaker identification. Also, almost all electronic devices are using
this technology too. Based on number of text, speaker identification divided into text dependent and text
independent. On many fields, text independent is mostly used because number of text is unlimited. So, text
independent is generally more challenging than text dependent. In this research, speaker identification text
independent with Indonesian speaker data was modelled with Vector Quantization (VQ). In this research
VQ with K-Means initialization was used. K-Means clustering also was used to initialize mean and
Hierarchical Agglomerative Clustering was used to identify K value for VQ. The best VQ accuracy was
59.67% when k was 5. According to the result, Indonesian language could be modelled by VQ. This
research can be developed using optimization method for VQ parameters such as Genetic Algorithm or
Particle Swarm Optimization.
This document provides an overview of different techniques for clustering categorical data. It discusses various clustering algorithms that have been used for categorical data, including K-modes, ROCK, COBWEB, and EM algorithms. It also reviews more recently developed algorithms for categorical data clustering, such as algorithms based on particle swarm optimization, rough set theory, and feature weighting schemes. The document concludes that clustering categorical data remains an important area of research, with opportunities to develop techniques that initialize cluster centers better.
A Competent and Empirical Model of Distributed ClusteringIRJET Journal
This document discusses distributed document clustering. It begins with an introduction to how documents are stored and indexed in computers. It then discusses different clustering algorithms like hierarchical and k-means clustering that are used to group similar documents. The document proposes a new framework for efficiently clustering text documents stored across different distributed resources. It argues that traditional clustering algorithms cannot perfectly cluster text data in decentralized systems. The framework uses properties of traditional algorithms with the ability to cluster in distributed systems.
Iaetsd a survey on one class clusteringIaetsd Iaetsd
This document presents a new method for performing one-to-many data linkage called the One Class Clustering Tree (OCCT). The OCCT builds a tree structure with inner nodes representing features of the first dataset and leaves representing similar features of the second dataset. It uses splitting criteria and pruning methods to perform the data linkage more accurately than existing indexing techniques. The OCCT approach induces a decision tree using a splitting criteria and performs prepruning to determine which branches to trim. It then compares entities to match them between the two datasets and produces a final result.
IDENTIFICATION AND INVESTIGATION OF THE USER SESSION FOR LAN CONNECTIVITY VIA...ijcseit
This paper mainly presents some technical discussions on the identification and analyze of “LAN usersessions”.
The identification of a user-session is non trivial. Classical methods approaches rely on
threshold based mechanisms. Threshold based techniques are very sensitive to the value chosen for the
threshold, which may be difficult to set correctly. Clustering techniques are used to define a novel
methodology to identify LAN user-sessions without requiring an a priori definition of threshold values. We
have defined a clustering based approach in detail, and also we discussed positive and negative of this
approach, and we apply it to real traffic traces. The proposed methodology is applied to artificially
generated traces to evaluate its benefits against traditional threshold based approaches. We also analyzed
the characteristics of user-sessions extracted by the clustering methodology from real traces and study
their statistical properties.
Different Similarity Measures for Text Classification Using KnnIOSR Journals
This document summarizes research on classifying textual data using the k-nearest neighbors (KNN) algorithm and different similarity measures. It explores generating 9 different vector representations of text documents and using KNN with similarity measures like Euclidean, Manhattan, squared Euclidean, etc. to classify documents. The researchers tested KNN on a Reuters news corpus with 5,485 training documents across 8 classes and found that normalization and k=4 produced the best accuracy of 94.47%. They conclude KNN with different similarity measures and vector representations is effective for multi-class text classification.
Density Based Clustering Approach for Solving the Software Component Restruct...IRJET Journal
This document presents research on using the DBSCAN clustering algorithm to solve the problem of software component restructuring. It begins with an abstract that introduces DBSCAN and describes how it can group related software components. It then provides background on software component clustering and describes DBSCAN in more detail. The methodology section outlines the 4 phases of the proposed approach: data collection and processing, clustering with DBSCAN, visualization and analysis, and final restructuring. Experimental results show that DBSCAN produces more evenly distributed clusters compared to fuzzy clustering. The conclusion is that DBSCAN is a better technique for software restructuring as it can identify clusters of varying shapes and sizes without specifying the number of clusters in advance.
Reviews on swarm intelligence algorithms for text document clusteringIRJET Journal
This document reviews swarm intelligence algorithms that have been used for text document clustering. It discusses how text clustering is an unsupervised learning technique that groups similar documents into clusters while separating dissimilar documents. Various swarm intelligence algorithms like particle swarm optimization, artificial bee colony, grey wolf optimizer, and krill herd have been applied to text document clustering problems. The document surveys previous research that has used these swarm intelligence algorithms for text clustering and discusses their advantages and limitations. It aims to provide readers an overview of the different swarm intelligence algorithms available for text document clustering applications.
Performance Analysis and Parallelization of CosineSimilarity of DocumentsIRJET Journal
This document discusses performance analysis and parallelization of the cosine similarity algorithm for calculating document similarity. It proposes an optimized algorithm that utilizes parallel computing to calculate cosine similarity for large sets of retrieved documents more efficiently. The conventional cosine similarity algorithm becomes inefficient for large document sets. The parallelized approach aims to enhance efficiency and reduce latency by processing more documents in less time. The document reviews related work applying techniques like parallelization, cosine similarity, and dimensionality reduction to problems involving document clustering, text summarization, and information retrieval.
Knowledge Graph and Similarity Based Retrieval Method for Query Answering SystemIRJET Journal
This document proposes a knowledge graph and question answering system to extract and analyze information from large volumes of unstructured data like annual reports. It discusses using natural language processing techniques like named entity recognition with spaCy and dependency parsing to extract entity-relation pairs from text and construct a knowledge graph. For question answering, it analyzes user queries with similar NLP approaches and then matches query triplets to the knowledge graph to retrieve answers, combining information retrieval and trained classifiers. The proposed system aims to provide faster understanding and analysis of complex, unstructured data for professionals.
Review of Existing Methods in K-means Clustering AlgorithmIRJET Journal
This document reviews existing methods for improving the K-means clustering algorithm. K-means is widely used but has limitations such as sensitivity to outliers and initial centroid selection. The document summarizes several proposed approaches, including using MapReduce to select initial centroids and form clusters for large datasets, reducing execution time by cutting off iterations, improving cluster quality by selecting centroids systematically, and using sampling techniques to reduce I/O and network costs. It concludes that improved algorithms address K-means limitations better than the traditional approach.
IRJET- Cluster Analysis for Effective Information Retrieval through Cohesive ...IRJET Journal
This document discusses using document clustering to improve information retrieval systems. It proposes a framework with four steps: 1) the information retrieval system retrieves documents based on a user query, 2) a similarity measure is used to determine document similarity, 3) the documents are clustered based on similarity, and 4) the clusters are ranked based on relevance to the query. The goal of clustering is to group relevant documents together to help users more easily find needed information. Different clustering algorithms are reviewed, noting that hierarchical clustering and overlapping clusters may improve search results over other methods.
Machine learning for text document classification-efficient classification ap...IAESIJAI
Numerous alternative methods for text classification have been created because of the increase in the amount of online text information available. The cosine similarity classifier is the most extensively utilized simple and efficient approach. It improves text classification performance. It is combined with estimated values provided by conventional classifiers such as Multinomial Naive Bayesian (MNB). Consequently, combining the similarity between a test document and a category with the estimated value for the category enhances the performance of the classifier. This approach provides a text document categorization method that is both efficient and effective. In addition, methods for determining the proper relationship between a set of words in a document and its document categorization is also obtained.
IRJET- Review of Existing Methods in K-Means Clustering AlgorithmIRJET Journal
The document reviews existing methods for the k-means clustering algorithm. It discusses how k-means clustering works and some of its limitations when dealing with large datasets, such as being dependent on the initial choice of centroids. It then proposes using Hadoop to overcome big data challenges and calculate preliminary centroids for k-means clustering in a distributed manner. Finally, it reviews different techniques that have been proposed in other research to improve k-means clustering, such as methods for selecting better initial centroids or determining the optimal number of clusters.
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
IRJET- Review on Information Retrieval for Desktop Search EngineIRJET Journal
This document summarizes techniques for desktop search engines, including feature extraction using entity recognition, query understanding using part-of-speech tagging and segmentation, and similarity measures for scoring and ranking documents. It discusses using ontologies, concept graphs, semantic networks, and vector space models to represent knowledge in documents. Feature extraction identifies entities that can be mapped to knowledge bases to infer meanings. Query understanding aims to determine intent regardless of technique used. Similarity is measured using approaches like comparing maximum common subgraphs between a document and query graphs.
This document discusses a text document classification system using machine learning algorithms. It aims to classify newspaper articles into different sections like business, sports, etc. The system involves preprocessing text data, training classification models using algorithms like KNN, Naive Bayes, SVM and random forest. Hyperparameter tuning is performed to improve model performance using techniques like k-fold cross validation and grid search. The models are evaluated on a BBC news dataset containing over 1400 articles in 5 categories. The goal is to design a multi-label text classification model with optimized hyperparameters.
Twitter Sentiment Analysis: An Unsupervised ApproachIRJET Journal
The document describes a study that performs sentiment analysis on Twitter data using an unsupervised machine learning technique. It discusses how Twitter data was collected and preprocessed, including removing stopwords and lemmatizing words. It then used the FastText word embedding model to represent words as vectors, which is suitable for unlabeled data. The K-Means clustering algorithm was implemented to group the Twitter data into clusters in an unsupervised manner and classify the tweets as positive, negative, or neutral sentiment.
Clustering Approach Recommendation System using Agglomerative AlgorithmIRJET Journal
The document discusses clustering approaches and agglomerative algorithms for recommendation systems. It proposes a new clustering approach based on agglomerative algorithms that uses simple calculations to identify clusters. Clustering algorithms aim to group similar objects into clusters while maximizing dissimilarity between objects in different clusters. The document reviews various hierarchical and partitioning clustering methods and algorithms presented in previous literature. It then proposes using an agglomerative clustering approach for recommendation systems that identifies clusters through simple calculations.
Clustering of Big Data Using Different Data-Mining TechniquesIRJET Journal
This document discusses clustering techniques for big data using different data mining approaches. It begins with an introduction to big data and some of its key characteristics like volume, variety, velocity, etc. It then discusses two main data mining techniques - clustering and classification. For clustering, it describes algorithms like K-means and bisecting K-means. It proposes a methodology using these algorithms with MapReduce for big data clustering. Several modules are implemented and results are presented with figures. It concludes that big data frameworks need to consider complex relationships in data and high performance platforms are required for big data mining.
Parallel KNN for Big Data using Adaptive IndexingIRJET Journal
This document presents an evaluation of different algorithms for performing parallel k-nearest neighbor (kNN) queries on big data using the MapReduce framework. It first discusses how kNN algorithms do not scale well for large datasets. It then reviews existing MapReduce-based kNN algorithms like H-BNLJ, H-zkNNJ, and RankReduce that improve performance by partitioning data and distributing computation. The document also proposes using an adaptive indexing technique with the RankReduce algorithm. An implementation of this approach on a airline on-time statistics dataset shows it achieves better precision and speed than other algorithms.
K Means Clustering Algorithm for Partitioning Data Sets Evaluated From Horizo...IOSR Journals
This document discusses using k-means clustering to partition datasets that have been generated through horizontal aggregation of data from multiple database tables. It provides background on horizontal aggregation techniques like pivot tables and describes the k-means clustering algorithm. The algorithm is applied as an example to cluster a sample dataset into two groups. The document concludes that k-means clustering can effectively partition large datasets produced by horizontal aggregations to facilitate further data mining analysis.
IRJET- Cluster Analysis for Effective Information Retrieval through Cohesive ...IRJET Journal
This document discusses using document clustering techniques to improve information retrieval systems. It proposes a framework with four steps: 1) the information retrieval system retrieves documents based on a user query, 2) a similarity measure is used to determine document similarity, 3) the documents are clustered based on similarity, and 4) the clusters are ranked based on relevance to the query. The document reviews different clustering algorithms and argues that clustering can help organize retrieval results and improve the user experience of finding relevant information.
MPSKM Algorithm to Cluster Uneven Dimensional Time Series Subspace DataIRJET Journal
The document describes a new algorithm called MPSKM that clusters uneven dimensional time series subspace data. The algorithm aims to select attribute ranks based on their involvement in the data set and identify global and local patterns. It automates determining the number of clusters and cluster centers. The algorithm calculates a rank matrix based on the sum of squared errors between attribute pairs to rank attributes. It then uses the ranks to transform the data dimensions before clustering. The algorithm is tested on weather data and shown to reduce iteration counts and error compared to traditional methods.
Study on Relavance Feature Selection MethodsIRJET Journal
This document summarizes research on feature selection methods. It discusses how feature selection is used to reduce dimensionality when working with large datasets that have thousands of variables. Several feature selection algorithms are examined, including ant colony optimization, quadratic programming, variable ranking using filter, wrapper and embedded methods, and fast correlation-based filtering with sequential forward selection. Feature selection can improve classification efficiency and understanding of data by identifying the most meaningful features.
Similar to IRJET- Diverse Approaches for Document Clustering in Product Development Analyzer (20)
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
1) The document discusses the Sungal Tunnel project in Jammu and Kashmir, India, which is being constructed using the New Austrian Tunneling Method (NATM).
2) NATM involves continuous monitoring during construction to adapt to changing ground conditions, and makes extensive use of shotcrete for temporary tunnel support.
3) The methodology section outlines the systematic geotechnical design process for tunnels according to Austrian guidelines, and describes the various steps of NATM tunnel construction including initial and secondary tunnel support.
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
This study examines the effect of response reduction factors (R factors) on reinforced concrete (RC) framed structures through nonlinear dynamic analysis. Three RC frame models with varying heights (4, 8, and 12 stories) were analyzed in ETABS software under different R factors ranging from 1 to 5. The results showed that displacement increased as the R factor decreased, indicating less linear behavior for lower R factors. Drift also decreased proportionally with increasing R factors from 1 to 5. Shear forces in the frames decreased with higher R factors. In general, R factors of 3 to 5 produced more satisfactory performance with less displacement and drift. The displacement variations between different building heights were consistent at different R factors. This study evaluated how R factors influence
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...IRJET Journal
This study compares the use of Stark Steel and TMT Steel as reinforcement materials in a two-way reinforced concrete slab. Mechanical testing is conducted to determine the tensile strength, yield strength, and other properties of each material. A two-way slab design adhering to codes and standards is executed with both materials. The performance is analyzed in terms of deflection, stability under loads, and displacement. Cost analyses accounting for material, durability, maintenance, and life cycle costs are also conducted. The findings provide insights into the economic and structural implications of each material for reinforcement selection and recommendations on the most suitable material based on the analysis.
Effect of Camber and Angles of Attack on Airfoil CharacteristicsIRJET Journal
This document discusses a study analyzing the effect of camber, position of camber, and angle of attack on the aerodynamic characteristics of airfoils. Sixteen modified asymmetric NACA airfoils were analyzed using computational fluid dynamics (CFD) by varying the camber, camber position, and angle of attack. The results showed the relationship between these parameters and the lift coefficient, drag coefficient, and lift to drag ratio. This provides insight into how changes in airfoil geometry impact aerodynamic performance.
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...IRJET Journal
This document reviews the progress and challenges of aluminum-based metal matrix composites (MMCs), focusing on their fabrication processes and applications. It discusses how various aluminum MMCs have been developed using reinforcements like borides, carbides, oxides, and nitrides to improve mechanical and wear properties. These composites have gained prominence for their lightweight, high-strength and corrosion resistance properties. The document also examines recent advancements in fabrication techniques for aluminum MMCs and their growing applications in industries such as aerospace and automotive. However, it notes that challenges remain around issues like improper mixing of reinforcements and reducing reinforcement agglomeration.
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...IRJET Journal
This document discusses research on using graph neural networks (GNNs) for dynamic optimization of public transportation networks in real-time. GNNs represent transit networks as graphs with nodes as stops and edges as connections. The GNN model aims to optimize networks using real-time data on vehicle locations, arrival times, and passenger loads. This helps increase mobility, decrease traffic, and improve efficiency. The system continuously trains and infers to adapt to changing transit conditions, providing decision support tools. While research has focused on performance, more work is needed on security, socio-economic impacts, contextual generalization of models, continuous learning approaches, and effective real-time visualization.
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...IRJET Journal
This document summarizes a research project that aims to compare the structural performance of conventional slab and grid slab systems in multi-story buildings using ETABS software. The study will analyze both symmetric and asymmetric building models under various loading conditions. Parameters like deflections, moments, shears, and stresses will be examined to evaluate the structural effectiveness of each slab type. The results will provide insights into the comparative behavior of conventional and grid slabs to help engineers and architects select appropriate slab systems based on building layouts and design requirements.
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...IRJET Journal
This document summarizes and reviews a research paper on the seismic response of reinforced concrete (RC) structures with plan and vertical irregularities, with and without infill walls. It discusses how infill walls can improve or reduce the seismic performance of RC buildings, depending on factors like wall layout, height distribution, connection to the frame, and relative stiffness of walls and frames. The reviewed research paper analyzes the behavior of infill walls, effects of vertical irregularities, and seismic performance of high-rise structures under linear static and dynamic analysis. It studies response characteristics like story drift, deflection and shear. The document also provides literature on similar research investigating the effects of infill walls, soft stories, plan irregularities, and different
This document provides a review of machine learning techniques used in Advanced Driver Assistance Systems (ADAS). It begins with an abstract that summarizes key applications of machine learning in ADAS, including object detection, recognition, and decision-making. The introduction discusses the integration of machine learning in ADAS and how it is transforming vehicle safety. The literature review then examines several research papers on topics like lightweight deep learning models for object detection and lane detection models using image processing. It concludes by discussing challenges and opportunities in the field, such as improving algorithm robustness and adaptability.
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...IRJET Journal
The document analyzes temperature and precipitation trends in Asosa District, Benishangul Gumuz Region, Ethiopia from 1993 to 2022 based on data from the local meteorological station. The results show:
1) The average maximum and minimum annual temperatures have generally decreased over time, with maximum temperatures decreasing by a factor of -0.0341 and minimum by -0.0152.
2) Mann-Kendall tests found the decreasing temperature trends to be statistically significant for annual maximum temperatures but not for annual minimum temperatures.
3) Annual precipitation in Asosa District showed a statistically significant increasing trend.
The conclusions recommend development planners account for rising summer precipitation and declining temperatures in
P.E.B. Framed Structure Design and Analysis Using STAAD ProIRJET Journal
This document discusses the design and analysis of pre-engineered building (PEB) framed structures using STAAD Pro software. It provides an overview of PEBs, including that they are designed off-site with building trusses and beams produced in a factory. STAAD Pro is identified as a key tool for modeling, analyzing, and designing PEBs to ensure their performance and safety under various load scenarios. The document outlines modeling structural parts in STAAD Pro, evaluating structural reactions, assigning loads, and following international design codes and standards. In summary, STAAD Pro is used to design and analyze PEB framed structures to ensure safety and code compliance.
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...IRJET Journal
This document provides a review of research on innovative fiber integration methods for reinforcing concrete structures. It discusses studies that have explored using carbon fiber reinforced polymer (CFRP) composites with recycled plastic aggregates to develop more sustainable strengthening techniques. It also examines using ultra-high performance fiber reinforced concrete to improve shear strength in beams. Additional topics covered include the dynamic responses of FRP-strengthened beams under static and impact loads, and the performance of preloaded CFRP-strengthened fiber reinforced concrete beams. The review highlights the potential of fiber composites to enable more sustainable and resilient construction practices.
Survey Paper on Cloud-Based Secured Healthcare SystemIRJET Journal
This document summarizes a survey on securing patient healthcare data in cloud-based systems. It discusses using technologies like facial recognition, smart cards, and cloud computing combined with strong encryption to securely store patient data. The survey found that healthcare professionals believe digitizing patient records and storing them in a centralized cloud system would improve access during emergencies and enable more efficient care compared to paper-based systems. However, ensuring privacy and security of patient data is paramount as healthcare incorporates these digital technologies.
Review on studies and research on widening of existing concrete bridgesIRJET Journal
This document summarizes several studies that have been conducted on widening existing concrete bridges. It describes a study from China that examined load distribution factors for a bridge widened with composite steel-concrete girders. It also outlines challenges and solutions for widening a bridge in the UAE, including replacing bearings and stitching the new and existing structures. Additionally, it discusses two bridge widening projects in New Zealand that involved adding precast beams and stitching to connect structures. Finally, safety measures and challenges for strengthening a historic bridge in Switzerland under live traffic are presented.
React based fullstack edtech web applicationIRJET Journal
The document describes the architecture of an educational technology web application built using the MERN stack. It discusses the frontend developed with ReactJS, backend with NodeJS and ExpressJS, and MongoDB database. The frontend provides dynamic user interfaces, while the backend offers APIs for authentication, course management, and other functions. MongoDB enables flexible data storage. The architecture aims to provide a scalable, responsive platform for online learning.
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...IRJET Journal
This paper proposes integrating Internet of Things (IoT) and blockchain technologies to help implement objectives of India's National Education Policy (NEP) in the education sector. The paper discusses how blockchain could be used for secure student data management, credential verification, and decentralized learning platforms. IoT devices could create smart classrooms, automate attendance tracking, and enable real-time monitoring. Blockchain would ensure integrity of exam processes and resource allocation, while smart contracts automate agreements. The paper argues this integration has potential to revolutionize education by making it more secure, transparent and efficient, in alignment with NEP goals. However, challenges like infrastructure needs, data privacy, and collaborative efforts are also discussed.
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.IRJET Journal
This document provides a review of research on the performance of coconut fibre reinforced concrete. It summarizes several studies that tested different volume fractions and lengths of coconut fibres in concrete mixtures with varying compressive strengths. The studies found that coconut fibre improved properties like tensile strength, toughness, crack resistance, and spalling resistance compared to plain concrete. Volume fractions of 2-5% and fibre lengths of 20-50mm produced the best results. The document concludes that using a 4-5% volume fraction of coconut fibres 30-40mm in length with M30-M60 grade concrete would provide benefits based on previous research.
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...IRJET Journal
The document discusses optimizing business management processes through automation using Microsoft Power Automate and artificial intelligence. It provides an overview of Power Automate's key components and features for automating workflows across various apps and services. The document then presents several scenarios applying automation solutions to common business processes like data entry, monitoring, HR, finance, customer support, and more. It estimates the potential time and cost savings from implementing automation for each scenario. Finally, the conclusion emphasizes the transformative impact of AI and automation tools on business processes and the need for ongoing optimization.
Multistoried and Multi Bay Steel Building Frame by using Seismic DesignIRJET Journal
The document describes the seismic design of a G+5 steel building frame located in Roorkee, India according to Indian codes IS 1893-2002 and IS 800. The frame was analyzed using the equivalent static load method and response spectrum method, and its response in terms of displacements and shear forces were compared. Based on the analysis, the frame was designed as a seismic-resistant steel structure according to IS 800:2007. The software STAAD Pro was used for the analysis and design.
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...IRJET Journal
This research paper explores using plastic waste as a sustainable and cost-effective construction material. The study focuses on manufacturing pavers and bricks using recycled plastic and partially replacing concrete with plastic alternatives. Initial results found that pavers and bricks made from recycled plastic demonstrate comparable strength and durability to traditional materials while providing environmental and cost benefits. Additionally, preliminary research indicates incorporating plastic waste as a partial concrete replacement significantly reduces construction costs without compromising structural integrity. The outcomes suggest adopting plastic waste in construction can address plastic pollution while optimizing costs, promoting more sustainable building practices.
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.
An In-Depth Exploration of Natural Language Processing: Evolution, Applicatio...DharmaBanothu
Natural language processing (NLP) has
recently garnered significant interest for the
computational representation and analysis of human
language. Its applications span multiple domains such
as machine translation, email spam detection,
information extraction, summarization, healthcare,
and question answering. This paper first delineates
four phases by examining various levels of NLP and
components of Natural Language Generation,
followed by a review of the history and progression of
NLP. Subsequently, we delve into the current state of
the art by presenting diverse NLP applications,
contemporary trends, and challenges. Finally, we
discuss some available datasets, models, and
evaluation metrics in NLP.
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