This document describes a parallel implementation of the Apriori algorithm for frequent itemset mining using MapReduce. The key steps are: (1) The Apriori algorithm is broken down into independent mapping tasks to count candidate itemset occurrences in parallel; (2) A MapReduce job is used for each iteration where the map function counts occurrences and the reduce function sums the counts; (3) Experimental results on real datasets show the approach achieves good speedup, scaleup, and can efficiently process large datasets in a distributed manner.
Scalable frequent itemset mining using heterogeneous computing par apriori a...ijdpsjournal
Association Rule mining is one of the dominant tasks of data mining, which concerns in finding frequent
itemsets in large volumes of data in order to produce summarized models of mined rules. These models are
extended to generate association rules in various applications such as e-commerce, bio-informatics,
associations between image contents and non image features, analysis of effectiveness of sales and retail
industry, etc. In the vast increasing databases, the major challenge is the frequent itemsets mining in a
very short period of time. In the case of increasing data, the time taken to process the data should be
almost constant. Since high performance computing has many processors, and many cores, consistent runtime
performance for such very large databases on association rules mining is achieved. We, therefore,
must rely on high performance parallel and/or distributed computing. In literature survey, we have studied
the sequential Apriori algorithms and identified the fundamental problems in sequential environment and
parallel environment. In our proposed ParApriori, we have proposed parallel algorithm for GPGPU, and
we have also done the results analysis of our GPU parallel algorithm. We find that proposed algorithm
improved the computing time, consistency in performance over the increasing load. The empirical analysis
of the algorithm also shows that efficiency and scalability is verified over the series of datasets
experimented on many core GPU platform.
This document proposes an approach to improve the efficiency of the Apriori algorithm for association rule mining. The Apriori algorithm is inefficient because it requires multiple scans of the transaction database to find frequent itemsets. The proposed approach aims to reduce this inefficiency in two ways: 1) It reduces the size of the transaction database by removing transactions where the transaction size is less than the candidate itemset size. 2) It scans only the relevant transactions for candidate itemset counting rather than the full database, by using transaction IDs of minimum support items from the first pass of the algorithm. An example is provided to demonstrate how the approach reduces the database and number of transactions scanned to generate frequent itemsets more efficiently than the standard Apriori
EXPERIMENTS ON HYPOTHESIS "FUZZY K-MEANS IS BETTER THAN K-MEANS FOR CLUSTERING"IJDKP
Clustering is one of the data mining techniques that have been around to discover business intelligence by grouping objects into clusters using a similarity measure. Clustering is an unsupervised learning process that has many utilities in real time applications in the fields of marketing, biology, libraries, insurance, city-planning, earthquake studies and document clustering. Latent trends and relationships among data objects can be unearthed using clustering algorithms. Many clustering algorithms came into existence. However, the quality of clusters has to be given paramount importance. The quality objective is to achieve
highest similarity between objects of same cluster and lowest similarity between objects of different clusters. In this context, we studied two widely used clustering algorithms such as the K-Means and Fuzzy K-Means. K-Means is an exclusive clustering algorithm while the Fuzzy K-Means is an overlapping clustering algorithm. In this paper we prove the hypothesis “Fuzzy K-Means is better than K-Means for Clustering” through both literature and empirical study. We built a prototype application to demonstrate the differences between the two clustering algorithms. The experiments are made on diabetes dataset
obtained from the UCI repository. The empirical results reveal that the performance of Fuzzy K-Means is better than that of K-means in terms of quality or accuracy of clusters. Thus, our empirical study proved the hypothesis “Fuzzy K-Means is better than K-Means for Clustering”.
Scalable Rough C-Means clustering using Firefly algorithm..................................................................1
Abhilash Namdev and B.K. Tripathy
Significance of Embedded Systems to IoT................................................................................................. 15
P. R. S. M. Lakshmi, P. Lakshmi Narayanamma and K. Santhi Sri
Cognitive Abilities, Information Literacy Knowledge and Retrieval Skills of Undergraduates: A
Comparison of Public and Private Universities in Nigeria ........................................................................ 24
Janet O. Adekannbi and Testimony Morenike Oluwayinka
Risk Assessment in Constructing Horseshoe Vault Tunnels using Fuzzy Technique................................ 48
Erfan Shafaghat and Mostafa Yousefi Rad
Evaluating the Adoption of Deductive Database Technology in Augmenting Criminal Intelligence in
Zimbabwe: Case of Zimbabwe Republic Police......................................................................................... 68
Mahlangu Gilbert, Furusa Samuel Simbarashe, Chikonye Musafare and Mugoniwa Beauty
Analysis of Petrol Pumps Reachability in Anand District of Gujarat ....................................................... 77
Nidhi Arora
The document summarizes several improved algorithms that aim to address the drawbacks of the Apriori algorithm for association rule mining. It discusses six different approaches: 1) An intersection and record filter approach that counts candidate support only in transactions of sufficient length and uses set intersection; 2) An approach using set size and frequency to prune insignificant candidates; 3) An approach that reduces the candidate set and memory usage by only searching frequent itemsets once to delete candidates; 4) A partitioning approach that divides the database; 5) An approach using vertical data format to reduce database scans; and 6) A distributed approach to parallelize the algorithm across machines.
This article got published in the Software Developer's Journal's February Edition.
It describes the use of MapReduce paradigm to design Clustering algorithms and explain three algorithms using MapReduce.
- K-Means Clustering
- Canopy Clustering
- MinHash Clustering
This document discusses online analytical processing (OLAP) for business intelligence using a 3D architecture. It proposes the Next Generation Greedy Dynamic Mix based OLAP algorithm (NGGDM-OLAP) which uses a mix of greedy and dynamic approaches for efficient data cube modeling and multidimensional query results. The algorithm constructs execution plans in a top-down manner by identifying the most beneficial view at each step. The document also describes OLAP system architecture, multidimensional data modeling, different OLAP analysis models, and concludes that integrating OLAP and data mining tools can benefit both areas.
Now a day enormous amount of data is getting explored through Internet of Things (IoT) as technologies
are advancing and people uses these technologies in day to day activities, this data is termed as Big Data
having its characteristics and challenges. Frequent Itemset Mining algorithms are aimed to disclose
frequent itemsets from transactional database but as the dataset size increases, it cannot be handled by
traditional frequent itemset mining. MapReduce programming model solves the problem of large datasets
but it has large communication cost which reduces execution efficiency. This proposed new pre-processed
k-means technique applied on BigFIM algorithm. ClustBigFIM uses hybrid approach, clustering using kmeans
algorithm to generate Clusters from huge datasets and Apriori and Eclat to mine frequent itemsets
from generated clusters using MapReduce programming model. Results shown that execution efficiency of
ClustBigFIM algorithm is increased by applying k-means clustering algorithm before BigFIM algorithm as
one of the pre-processing technique.
Scalable frequent itemset mining using heterogeneous computing par apriori a...ijdpsjournal
Association Rule mining is one of the dominant tasks of data mining, which concerns in finding frequent
itemsets in large volumes of data in order to produce summarized models of mined rules. These models are
extended to generate association rules in various applications such as e-commerce, bio-informatics,
associations between image contents and non image features, analysis of effectiveness of sales and retail
industry, etc. In the vast increasing databases, the major challenge is the frequent itemsets mining in a
very short period of time. In the case of increasing data, the time taken to process the data should be
almost constant. Since high performance computing has many processors, and many cores, consistent runtime
performance for such very large databases on association rules mining is achieved. We, therefore,
must rely on high performance parallel and/or distributed computing. In literature survey, we have studied
the sequential Apriori algorithms and identified the fundamental problems in sequential environment and
parallel environment. In our proposed ParApriori, we have proposed parallel algorithm for GPGPU, and
we have also done the results analysis of our GPU parallel algorithm. We find that proposed algorithm
improved the computing time, consistency in performance over the increasing load. The empirical analysis
of the algorithm also shows that efficiency and scalability is verified over the series of datasets
experimented on many core GPU platform.
This document proposes an approach to improve the efficiency of the Apriori algorithm for association rule mining. The Apriori algorithm is inefficient because it requires multiple scans of the transaction database to find frequent itemsets. The proposed approach aims to reduce this inefficiency in two ways: 1) It reduces the size of the transaction database by removing transactions where the transaction size is less than the candidate itemset size. 2) It scans only the relevant transactions for candidate itemset counting rather than the full database, by using transaction IDs of minimum support items from the first pass of the algorithm. An example is provided to demonstrate how the approach reduces the database and number of transactions scanned to generate frequent itemsets more efficiently than the standard Apriori
EXPERIMENTS ON HYPOTHESIS "FUZZY K-MEANS IS BETTER THAN K-MEANS FOR CLUSTERING"IJDKP
Clustering is one of the data mining techniques that have been around to discover business intelligence by grouping objects into clusters using a similarity measure. Clustering is an unsupervised learning process that has many utilities in real time applications in the fields of marketing, biology, libraries, insurance, city-planning, earthquake studies and document clustering. Latent trends and relationships among data objects can be unearthed using clustering algorithms. Many clustering algorithms came into existence. However, the quality of clusters has to be given paramount importance. The quality objective is to achieve
highest similarity between objects of same cluster and lowest similarity between objects of different clusters. In this context, we studied two widely used clustering algorithms such as the K-Means and Fuzzy K-Means. K-Means is an exclusive clustering algorithm while the Fuzzy K-Means is an overlapping clustering algorithm. In this paper we prove the hypothesis “Fuzzy K-Means is better than K-Means for Clustering” through both literature and empirical study. We built a prototype application to demonstrate the differences between the two clustering algorithms. The experiments are made on diabetes dataset
obtained from the UCI repository. The empirical results reveal that the performance of Fuzzy K-Means is better than that of K-means in terms of quality or accuracy of clusters. Thus, our empirical study proved the hypothesis “Fuzzy K-Means is better than K-Means for Clustering”.
Scalable Rough C-Means clustering using Firefly algorithm..................................................................1
Abhilash Namdev and B.K. Tripathy
Significance of Embedded Systems to IoT................................................................................................. 15
P. R. S. M. Lakshmi, P. Lakshmi Narayanamma and K. Santhi Sri
Cognitive Abilities, Information Literacy Knowledge and Retrieval Skills of Undergraduates: A
Comparison of Public and Private Universities in Nigeria ........................................................................ 24
Janet O. Adekannbi and Testimony Morenike Oluwayinka
Risk Assessment in Constructing Horseshoe Vault Tunnels using Fuzzy Technique................................ 48
Erfan Shafaghat and Mostafa Yousefi Rad
Evaluating the Adoption of Deductive Database Technology in Augmenting Criminal Intelligence in
Zimbabwe: Case of Zimbabwe Republic Police......................................................................................... 68
Mahlangu Gilbert, Furusa Samuel Simbarashe, Chikonye Musafare and Mugoniwa Beauty
Analysis of Petrol Pumps Reachability in Anand District of Gujarat ....................................................... 77
Nidhi Arora
The document summarizes several improved algorithms that aim to address the drawbacks of the Apriori algorithm for association rule mining. It discusses six different approaches: 1) An intersection and record filter approach that counts candidate support only in transactions of sufficient length and uses set intersection; 2) An approach using set size and frequency to prune insignificant candidates; 3) An approach that reduces the candidate set and memory usage by only searching frequent itemsets once to delete candidates; 4) A partitioning approach that divides the database; 5) An approach using vertical data format to reduce database scans; and 6) A distributed approach to parallelize the algorithm across machines.
This article got published in the Software Developer's Journal's February Edition.
It describes the use of MapReduce paradigm to design Clustering algorithms and explain three algorithms using MapReduce.
- K-Means Clustering
- Canopy Clustering
- MinHash Clustering
This document discusses online analytical processing (OLAP) for business intelligence using a 3D architecture. It proposes the Next Generation Greedy Dynamic Mix based OLAP algorithm (NGGDM-OLAP) which uses a mix of greedy and dynamic approaches for efficient data cube modeling and multidimensional query results. The algorithm constructs execution plans in a top-down manner by identifying the most beneficial view at each step. The document also describes OLAP system architecture, multidimensional data modeling, different OLAP analysis models, and concludes that integrating OLAP and data mining tools can benefit both areas.
Now a day enormous amount of data is getting explored through Internet of Things (IoT) as technologies
are advancing and people uses these technologies in day to day activities, this data is termed as Big Data
having its characteristics and challenges. Frequent Itemset Mining algorithms are aimed to disclose
frequent itemsets from transactional database but as the dataset size increases, it cannot be handled by
traditional frequent itemset mining. MapReduce programming model solves the problem of large datasets
but it has large communication cost which reduces execution efficiency. This proposed new pre-processed
k-means technique applied on BigFIM algorithm. ClustBigFIM uses hybrid approach, clustering using kmeans
algorithm to generate Clusters from huge datasets and Apriori and Eclat to mine frequent itemsets
from generated clusters using MapReduce programming model. Results shown that execution efficiency of
ClustBigFIM algorithm is increased by applying k-means clustering algorithm before BigFIM algorithm as
one of the pre-processing technique.
An Improved Frequent Itemset Generation Algorithm Based On Correspondence cscpconf
Association rules play a very vital role in the present day market that especially involves generation of maximal frequent itemsets in an efficient way. The efficiency of association rule is determined by the number of database scans required to generate the frequent itemsets. This in turn is proportional to the time, which will lead to the faster computation of the frequent itemsets. In this paper, a single scan algorithm which makes use of the mapping of the item numbers and array indexing to achieve the generation of the frequent item sets dynamically and faster. The proposed algorithm is an incremental algorithm in that it generates frequent itemsets as and when the data is entered into the database
This document discusses data structures and their role in organizing data efficiently for computer programs. It defines key concepts like abstract data types, algorithms, and problems. It also provides examples to illustrate selecting the appropriate data structure based on the operations and constraints of a problem. A banking application is used to demonstrate how hash tables are suitable because they allow extremely fast searching by account numbers while also supporting efficient insertion and deletion. B-trees are shown to be better than hash tables for a city database because they enable fast range queries in addition to exact searches. Overall, the document emphasizes that each data structure has costs and benefits, and a careful analysis is needed to determine the best structure for a given problem.
IRJET- Privacy Preservation using Apache SparkIRJET Journal
This document discusses using Apache Spark to perform privacy preservation on big data through faster data anonymization techniques. It proposes implementing common anonymization algorithms like k-anonymity, l-diversity, and t-closeness using Spark's in-memory processing capabilities to overcome limitations of previous Hadoop-based approaches. The key advantages of Spark are its faster processing speed due to avoiding disk I/O and ability to support streaming data and real-time processing. The implementation section describes how k-anonymity, l-diversity, and t-closeness algorithms can be applied to anonymize medical datasets in Spark to preserve patient privacy while minimizing information loss.
Top-K Dominating Queries on Incomplete Data with Prioritiesijtsrd
This document discusses algorithms for finding the top-K dominating queries on incomplete datasets. It proposes using a skyline-based algorithm that incorporates priority values for each dimension. This allows the algorithm to determine dominance even when the values are missing for some dimensions. It works by bucketing the data based on bit representations, finding the local skylines within each bucket, and then calculating scores for objects based on their dominance over other objects while considering the priority of dimensions. The top-K objects with the highest scores are then returned as the results. This approach provides more accurate outputs for applications like movie recommendations by allowing users to specify dimension priorities.
This document proposes an approach for automatic programming using deep learning. It describes a hybrid method using generative recurrent neural networks trained on source code to generate predictions, which are then used to build abstract syntax trees (ASTs) representing potential code structures. The ASTs are combined and mutated using techniques from genetic programming and random forests. Experimental results found the method was able to generate functions like computing the square root using an iterative method, demonstrating it can generalize logical algorithms from short descriptions. The document outlines the scope of the problem and approach, and describes using a GitHub scraper to collect a dataset of relevant Python source code files to train and evaluate the models.
A PREFIXED-ITEMSET-BASED IMPROVEMENT FOR APRIORI ALGORITHMcsandit
Association rules is a very important part of data mining. It is used to find the interesting patterns from transaction databases. Apriori algorithm is one of the most classical algorithms
of association rules, but it has the bottleneck in efficiency. In this article, we proposed a prefixed-itemset-based data structure for candidate itemset generation, with the help of the structure we managed to improve the efficiency of the classical Apriori algorithm.
This document summarizes an article from the International Journal of Computer Engineering and Technology (IJCET) that proposes a method for divisive hierarchical clustering using partitioning methods. It begins with an abstract that introduces hierarchical clustering and partitioning methods, and how the paper uses partitioning with hierarchical clustering to form improved clusters. The document then provides background on hierarchical clustering and partitioning clustering methods. It summarizes related work on hierarchical clustering for data mining and automatically labeling hierarchical clusters. It concludes by summarizing the paper's proposal to use dynamic closest pair data structures to perform fast hierarchical clustering with insertions and deletions in logarithmic time.
The document proposes an efficient tree-based structure and algorithm for mining frequent patterns from transactional databases that improves upon the CATS algorithm. It constructs a single condensed tree for each frequent item using a pre-order traversal of the original CATS tree, whereas CATS-FELINE constructs conditional condensed trees recursively. This reduces memory usage by collapsing separate nodes for infrequent items under the root node. Experimental results show the proposed algorithm has faster execution times than CATS-FELINE for different datasets and minimum support values.
Hybrid Algorithm for Clustering Mixed Data SetsIOSR Journals
This document summarizes a hybrid algorithm for clustering mixed data sets that was proposed in reference [1]. The algorithm uses a genetic k-means approach to cluster both numeric and categorical data, overcoming limitations of other algorithms that can only handle one data type. It aims to minimize the total within-cluster variation to group similar objects. The selection operator uses proportional selection to determine the population for the next generation based on each solution's probability and fitness. The algorithm was reviewed, implemented in a prototype application, and found to improve performance compared to other related clustering algorithms like GKMODE and IGKA that also handle mixed data types.
Achieving Algorithmic Transparency with Shapley Additive Explanations (H2O Lo...Sri Ambati
Abstract:
Explainability in the age of the EU GDPR is becoming an increasingly pertinent consideration for Machine Learning. At QuantumBlack, we address the traditional Accuracy vs. Interpretability trade-off, by leveraging modern XAI techniques such as LIME and SHAP, to enable individualised explanations without necessary limiting the utility and performance of the otherwise ‘black-box’ models. The talk focuses on Shapley additive explanations (Lundberg et al. 2017) that integrate Shapley values from the Game Theory for consistent and locally accurate explanations; provides illustrative examples and touches upon the wider XAI theory.
Bio:
Dr Torgyn Shaikhina is a Data Scientist at QuantumBlack, STEM Ambassador, and the founder of the Next Generation Programmers outreach initiative. Her background is in decision support systems for Healthcare and Biomedical Engineering with a focus on Machine Learning with limited information.
This document compares and evaluates several algorithms for mining association rules from frequent itemsets in transactional databases. It summarizes the Apriori, FP-Growth, Closure and MaxClosure algorithms, and experimentally compares their performance based on factors like number of transactions, minimum support, and execution time. The paper finds that algorithms like FP-Growth that avoid candidate generation perform better than Apriori, which generates a large number of candidate itemsets and requires multiple database scans.
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.
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.
GraphLab is a framework for parallel machine learning that represents data as a graph and uses shared tables. It allows users to define update, fold, and merge functions to modify vertex/edge states and aggregate data in shared tables. The GraphLab toolkit includes applications for topic modeling, graph analytics, clustering, collaborative filtering, and computer vision. Users can run GraphLab on Amazon EC2 by satisfying dependencies, compiling, and running examples like stochastic gradient descent for collaborative filtering on Netflix data.
International Journal of Computational Engineering Research(IJCER)ijceronline
This document summarizes a research paper that proposes a novel approach to improve the detection rate and search efficiency of signature-based network intrusion detection systems (NIDS). The approach uses data mining and classification algorithms like C4.5 and ensemble algorithms like MadaBoost to improve detection rates. It also uses a modified signature apriori algorithm to more efficiently search for signatures of related attacks based on known signatures, in order to improve search efficiency. The full paper describes these approaches in more technical detail and evaluates their effectiveness at improving NIDS performance.
The document summarizes 10 influential data mining algorithms:
1. C4.5 decision tree algorithm and its successor C5.0, which can construct classifiers as decision trees or rulesets.
2. K-means clustering algorithm, an iterative algorithm that partitions data into k clusters based on minimizing distances between data points and cluster centers.
3. Additional algorithms covered include SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. These algorithms cover important data mining tasks such as classification, clustering, association analysis, and link mining.
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.
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.
The document discusses machine learning techniques for graphs and graph-parallel computing. It describes how graphs can model real-world data with entities as vertices and relationships as edges. Common machine learning tasks on graphs include identifying influential entities, finding communities, modeling dependencies, and predicting user behavior. The document introduces the concept of graph-parallel programming models that allow algorithms to be expressed by having each vertex perform computations based on its local neighborhood. It presents examples of graph algorithms like PageRank, product recommendations, and identifying leaders that can be implemented in a graph-parallel manner. Finally, it discusses challenges of analyzing large real-world graphs and how systems like GraphLab address these challenges through techniques like vertex-cuts and asynchronous execution.
Implementation of Improved Apriori Algorithm on Large Dataset using HadoopBRNSSPublicationHubI
This document describes research on improving the Apriori algorithm for association rule mining on large datasets using Hadoop. The researchers implemented an improved Apriori algorithm that uses MapReduce on Hadoop to reduce the number of database scans needed. They tested the proposed algorithm on various datasets and found it had faster execution times and used less memory compared to the traditional Apriori algorithm.
The premise of this paper is to discover frequent patterns by the use of data grids in WEKA 3.8 environment. Workload imbalance occurs due to the dynamic nature of the grid computing hence data grids are used for the creation and validation of data. Association rules are used to extract the useful information from the large database. In this paper the researcher generate the best rules by using WEKA 3.8 for better performance. WEKA 3.8 is used to accomplish best rules and implementation of various algorithms.
An Improved Frequent Itemset Generation Algorithm Based On Correspondence cscpconf
Association rules play a very vital role in the present day market that especially involves generation of maximal frequent itemsets in an efficient way. The efficiency of association rule is determined by the number of database scans required to generate the frequent itemsets. This in turn is proportional to the time, which will lead to the faster computation of the frequent itemsets. In this paper, a single scan algorithm which makes use of the mapping of the item numbers and array indexing to achieve the generation of the frequent item sets dynamically and faster. The proposed algorithm is an incremental algorithm in that it generates frequent itemsets as and when the data is entered into the database
This document discusses data structures and their role in organizing data efficiently for computer programs. It defines key concepts like abstract data types, algorithms, and problems. It also provides examples to illustrate selecting the appropriate data structure based on the operations and constraints of a problem. A banking application is used to demonstrate how hash tables are suitable because they allow extremely fast searching by account numbers while also supporting efficient insertion and deletion. B-trees are shown to be better than hash tables for a city database because they enable fast range queries in addition to exact searches. Overall, the document emphasizes that each data structure has costs and benefits, and a careful analysis is needed to determine the best structure for a given problem.
IRJET- Privacy Preservation using Apache SparkIRJET Journal
This document discusses using Apache Spark to perform privacy preservation on big data through faster data anonymization techniques. It proposes implementing common anonymization algorithms like k-anonymity, l-diversity, and t-closeness using Spark's in-memory processing capabilities to overcome limitations of previous Hadoop-based approaches. The key advantages of Spark are its faster processing speed due to avoiding disk I/O and ability to support streaming data and real-time processing. The implementation section describes how k-anonymity, l-diversity, and t-closeness algorithms can be applied to anonymize medical datasets in Spark to preserve patient privacy while minimizing information loss.
Top-K Dominating Queries on Incomplete Data with Prioritiesijtsrd
This document discusses algorithms for finding the top-K dominating queries on incomplete datasets. It proposes using a skyline-based algorithm that incorporates priority values for each dimension. This allows the algorithm to determine dominance even when the values are missing for some dimensions. It works by bucketing the data based on bit representations, finding the local skylines within each bucket, and then calculating scores for objects based on their dominance over other objects while considering the priority of dimensions. The top-K objects with the highest scores are then returned as the results. This approach provides more accurate outputs for applications like movie recommendations by allowing users to specify dimension priorities.
This document proposes an approach for automatic programming using deep learning. It describes a hybrid method using generative recurrent neural networks trained on source code to generate predictions, which are then used to build abstract syntax trees (ASTs) representing potential code structures. The ASTs are combined and mutated using techniques from genetic programming and random forests. Experimental results found the method was able to generate functions like computing the square root using an iterative method, demonstrating it can generalize logical algorithms from short descriptions. The document outlines the scope of the problem and approach, and describes using a GitHub scraper to collect a dataset of relevant Python source code files to train and evaluate the models.
A PREFIXED-ITEMSET-BASED IMPROVEMENT FOR APRIORI ALGORITHMcsandit
Association rules is a very important part of data mining. It is used to find the interesting patterns from transaction databases. Apriori algorithm is one of the most classical algorithms
of association rules, but it has the bottleneck in efficiency. In this article, we proposed a prefixed-itemset-based data structure for candidate itemset generation, with the help of the structure we managed to improve the efficiency of the classical Apriori algorithm.
This document summarizes an article from the International Journal of Computer Engineering and Technology (IJCET) that proposes a method for divisive hierarchical clustering using partitioning methods. It begins with an abstract that introduces hierarchical clustering and partitioning methods, and how the paper uses partitioning with hierarchical clustering to form improved clusters. The document then provides background on hierarchical clustering and partitioning clustering methods. It summarizes related work on hierarchical clustering for data mining and automatically labeling hierarchical clusters. It concludes by summarizing the paper's proposal to use dynamic closest pair data structures to perform fast hierarchical clustering with insertions and deletions in logarithmic time.
The document proposes an efficient tree-based structure and algorithm for mining frequent patterns from transactional databases that improves upon the CATS algorithm. It constructs a single condensed tree for each frequent item using a pre-order traversal of the original CATS tree, whereas CATS-FELINE constructs conditional condensed trees recursively. This reduces memory usage by collapsing separate nodes for infrequent items under the root node. Experimental results show the proposed algorithm has faster execution times than CATS-FELINE for different datasets and minimum support values.
Hybrid Algorithm for Clustering Mixed Data SetsIOSR Journals
This document summarizes a hybrid algorithm for clustering mixed data sets that was proposed in reference [1]. The algorithm uses a genetic k-means approach to cluster both numeric and categorical data, overcoming limitations of other algorithms that can only handle one data type. It aims to minimize the total within-cluster variation to group similar objects. The selection operator uses proportional selection to determine the population for the next generation based on each solution's probability and fitness. The algorithm was reviewed, implemented in a prototype application, and found to improve performance compared to other related clustering algorithms like GKMODE and IGKA that also handle mixed data types.
Achieving Algorithmic Transparency with Shapley Additive Explanations (H2O Lo...Sri Ambati
Abstract:
Explainability in the age of the EU GDPR is becoming an increasingly pertinent consideration for Machine Learning. At QuantumBlack, we address the traditional Accuracy vs. Interpretability trade-off, by leveraging modern XAI techniques such as LIME and SHAP, to enable individualised explanations without necessary limiting the utility and performance of the otherwise ‘black-box’ models. The talk focuses on Shapley additive explanations (Lundberg et al. 2017) that integrate Shapley values from the Game Theory for consistent and locally accurate explanations; provides illustrative examples and touches upon the wider XAI theory.
Bio:
Dr Torgyn Shaikhina is a Data Scientist at QuantumBlack, STEM Ambassador, and the founder of the Next Generation Programmers outreach initiative. Her background is in decision support systems for Healthcare and Biomedical Engineering with a focus on Machine Learning with limited information.
This document compares and evaluates several algorithms for mining association rules from frequent itemsets in transactional databases. It summarizes the Apriori, FP-Growth, Closure and MaxClosure algorithms, and experimentally compares their performance based on factors like number of transactions, minimum support, and execution time. The paper finds that algorithms like FP-Growth that avoid candidate generation perform better than Apriori, which generates a large number of candidate itemsets and requires multiple database scans.
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.
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.
GraphLab is a framework for parallel machine learning that represents data as a graph and uses shared tables. It allows users to define update, fold, and merge functions to modify vertex/edge states and aggregate data in shared tables. The GraphLab toolkit includes applications for topic modeling, graph analytics, clustering, collaborative filtering, and computer vision. Users can run GraphLab on Amazon EC2 by satisfying dependencies, compiling, and running examples like stochastic gradient descent for collaborative filtering on Netflix data.
International Journal of Computational Engineering Research(IJCER)ijceronline
This document summarizes a research paper that proposes a novel approach to improve the detection rate and search efficiency of signature-based network intrusion detection systems (NIDS). The approach uses data mining and classification algorithms like C4.5 and ensemble algorithms like MadaBoost to improve detection rates. It also uses a modified signature apriori algorithm to more efficiently search for signatures of related attacks based on known signatures, in order to improve search efficiency. The full paper describes these approaches in more technical detail and evaluates their effectiveness at improving NIDS performance.
The document summarizes 10 influential data mining algorithms:
1. C4.5 decision tree algorithm and its successor C5.0, which can construct classifiers as decision trees or rulesets.
2. K-means clustering algorithm, an iterative algorithm that partitions data into k clusters based on minimizing distances between data points and cluster centers.
3. Additional algorithms covered include SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. These algorithms cover important data mining tasks such as classification, clustering, association analysis, and link mining.
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.
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.
The document discusses machine learning techniques for graphs and graph-parallel computing. It describes how graphs can model real-world data with entities as vertices and relationships as edges. Common machine learning tasks on graphs include identifying influential entities, finding communities, modeling dependencies, and predicting user behavior. The document introduces the concept of graph-parallel programming models that allow algorithms to be expressed by having each vertex perform computations based on its local neighborhood. It presents examples of graph algorithms like PageRank, product recommendations, and identifying leaders that can be implemented in a graph-parallel manner. Finally, it discusses challenges of analyzing large real-world graphs and how systems like GraphLab address these challenges through techniques like vertex-cuts and asynchronous execution.
Implementation of Improved Apriori Algorithm on Large Dataset using HadoopBRNSSPublicationHubI
This document describes research on improving the Apriori algorithm for association rule mining on large datasets using Hadoop. The researchers implemented an improved Apriori algorithm that uses MapReduce on Hadoop to reduce the number of database scans needed. They tested the proposed algorithm on various datasets and found it had faster execution times and used less memory compared to the traditional Apriori algorithm.
The premise of this paper is to discover frequent patterns by the use of data grids in WEKA 3.8 environment. Workload imbalance occurs due to the dynamic nature of the grid computing hence data grids are used for the creation and validation of data. Association rules are used to extract the useful information from the large database. In this paper the researcher generate the best rules by using WEKA 3.8 for better performance. WEKA 3.8 is used to accomplish best rules and implementation of various algorithms.
CLUSTBIGFIM-FREQUENT ITEMSET MINING OF BIG DATA USING PRE-PROCESSING BASED ON...ijfcstjournal
This document describes the ClustBigFIM algorithm for frequent itemset mining of big data using pre-processing based on the MapReduce framework. The ClustBigFIM algorithm first applies k-means clustering to generate clusters from large datasets. It then mines frequent itemsets from the generated clusters using the Apriori and Eclat algorithms within the MapReduce programming model. Experimental results on several datasets show that the ClustBigFIM algorithm increases execution efficiency compared to the BigFIM algorithm by applying k-means clustering as a pre-processing step before frequent itemset mining.
Comparative analysis of association rule generation algorithms in data streamsIJCI JOURNAL
This document summarizes the results of an experiment that compares three algorithms for generating association rules from data streams: Association Outliers, Frequent Item Sets, and Supervised Association Rule. The algorithms were tested on partitioned windows of a connectivity dataset containing 1,000 to 10,000 instances. Association rules and execution time were used as performance metrics. The Frequent Item Set algorithm generated more rules faster than the other two algorithms across all window sizes and data volumes tested.
Hadoop Map-Reduce To Generate Frequent Item Set on Large Datasets Using Impro...BRNSSPublicationHubI
This document presents an improved Apriori algorithm for generating frequent item sets on large datasets using Hadoop MapReduce. The classical Apriori algorithm suffers from repeated database scans, high candidate generation costs, and memory issues. The proposed improved Apriori algorithm aims to address these issues by leveraging Hadoop MapReduce to parallelize the processing and reduce unnecessary database scans. It presents the pseudocode for the classical and improved algorithms. The improved algorithm is evaluated to show it provides better performance than the classical Apriori algorithm in terms of time and number of iterations required.
A SERIAL COMPUTING MODEL OF AGENT ENABLED MINING OF GLOBALLY STRONG ASSOCIATI...ijcsa
The intelligent agent based model is a popular approach in constructing Distributed Data Mining (DDM) systems to address scalable mining over large scale and ever increasing distributed data. In an agent based
distributed system, variety of agents coordinate and communicate with each other to perform the various
tasks of the Data Mining (DM) process. In this study a serial computing mode of a multi-agent system
(MAS) called Agent enabled Mining of Globally Strong Association Rules (AeMGSAR) is presented based
on the serial itinerary of the mobile agents. A Running environment is also designed for the implementation and performance study of AeMGSAR system.
An improved apriori algorithm for association rulesijnlc
There are several mining algorithms of association rules. One of the most popular algorithms is Apriori
that is used to extract frequent itemsets from large database and getting the association rule for
discovering the knowledge. Based on this algorithm, this paper indicates the limitation of the original
Apriori algorithm of wasting time for scanning the whole database searching on the frequent itemsets, and
presents an improvement on Apriori by reducing that wasted time depending on scanning only some
transactions. The paper shows by experimental results with several groups of transactions, and with
several values of minimum support that applied on the original Apriori and our implemented improved
Apriori that our improved Apriori reduces the time consumed by 67.38% in comparison with the original
Apriori, and makes the Apriori algorithm more efficient and less time consuming
Discovering Frequent Patterns with New Mining ProcedureIOSR Journals
This document provides a summary of existing algorithms for discovering frequent patterns in transactional datasets. It begins with an introduction to the problem of mining frequent itemsets and association rules. It then describes the Apriori algorithm, which is a seminal and classical level-wise algorithm for mining frequent itemsets. The document notes some limitations of Apriori when applied to large datasets, including increased computational cost due to many database scans and large candidate sets. It then briefly describes the FP-Growth algorithm as an alternative pattern growth approach. The remainder of the document focuses on improvements made to Apriori, including the Direct Hashing and Pruning (DHP) algorithm, which aims to reduce the candidate set size to improve efficiency.
This document discusses using the R programming language and RHadoop libraries to perform association rule mining on big data stored in Hadoop. It first provides background on big data, association rule mining, and integrating R with Hadoop using RHadoop. It then describes setting up an 8-node Hadoop cluster using Ambari and installing RHadoop libraries to enable R scripts to run MapReduce jobs on the cluster. The goal is to use R and RHadoop to analyze a training dataset and discover interesting association rules.
This document discusses data structures and algorithms. It provides grading schemes for theory and lab components. It acknowledges reference sources used to prepare the lecture. Key points covered include: what data structures are and why they are important for organizing data efficiently; characteristics of good data structures like time and space complexity; definitions of algorithms and examples like searching and sorting; and algorithmic notations used to describe processes like linear and binary search of arrays.
Existing parallel digging calculations for visit itemsets do not have a component that empowers programmed parallelization, stack adjusting, information conveyance, and adaptation to non-critical failure on substantial bunches. As an answer for this issue, we outline a parallel incessant itemsets mining calculation called FiDoop utilizing the MapReduce programming model. To accomplish compacted capacity and abstain from building contingent example bases, FiDoop joins the incessant things Ultrametric tree, as opposed to ordinary FP trees. In FiDoop, three MapReduce occupations are actualized to finish the mining undertaking. In the essential third MapReduce work, the mappers autonomously disintegrate itemsets, the reducers perform mix activities by building little Ultrametric trees, and the genuine mining of these trees independently. We actualize FiDoop on our in-house Hadoop group. We demonstrate that FiDoop on the group is touchy to information dissemination and measurements, in light of the fact that itemsets with various lengths have diverse decay and development costs. To enhance FiDoop's execution, we build up a workload adjust metric to quantify stack adjust over the group's registering hubs. We create FiDoop-HD, an augmentation of FiDoop, to accelerate the digging execution for high-dimensional information investigation. Broad tests utilizing genuine heavenly phantom information exhibit that our proposed arrangement is productive and versatile.
Implementation of p pic algorithm in map reduce to handle big dataeSAT Publishing House
This document presents an implementation of the p-PIC clustering algorithm using the MapReduce framework to handle big data. P-PIC is a parallel version of the Power Iteration Clustering (PIC) algorithm that is able to cluster large datasets in a distributed environment. The document first provides background on PIC and challenges with scaling to big data. It then describes how p-PIC addresses these challenges using MPI for parallelization. The design of implementing p-PIC within MapReduce is presented, including the map and reduce functions. Experimental results on synthetic datasets up to 100,000 records show that p-PIC using MapReduce has increased performance and scalability compared to the original p-PIC implementation using MPI.
Top Down Approach to find Maximal Frequent Item Sets using Subset Creationcscpconf
Association rule has been an area of active research in the field of knowledge discovery. Data
mining researchers had improved upon the quality of association rule mining for business
development by incorporating influential factors like value (utility), quantity of items sold
(weight) and more for the mining of association patterns. In this paper, we propose an efficient
approach to find maximal frequent item set first. Most of the algorithms in literature used to find
minimal frequent item first, then with the help of minimal frequent item sets derive the maximal
frequent item sets. These methods consume more time to find maximal frequent item sets. To
overcome this problem, we propose a navel approach to find maximal frequent item set directly using the concepts of subsets. The proposed method is found to be efficient in finding maximal frequent item sets.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability
Generating Non-redundant Multilevel Association Rules Using Min-max Exact Rules IJECEIAES
Association Rule mining plays an important role in the discovery of knowledge and information. Association Rule mining discovers huge number of rules for any dataset for different support and confidence values, among this many of them are redundant, especially in the case of multi-level datasets. Mining non-redundant Association Rules in multi-level dataset is a big concern in field of Data mining. In this paper, we present a definition for redundancy and a concise representation called Reliable Exact basis for representing non-redundant Association Rules from multi-level datasets. The given non-redundant Association Rules are loss less representation for any datasets.
An Improved Differential Evolution Algorithm for Data Stream ClusteringIJECEIAES
A Few algorithms were actualized by the analysts for performing clustering of data streams. Most of these algorithms require that the number of clusters (K) has to be fixed by the customer based on input data and it can be kept settled all through the clustering process. Stream clustering has faced few difficulties in picking up K. In this paper, we propose an efficient approach for data stream clustering by embracing an Improved Differential Evolution (IDE) algorithm. The IDE algorithm is one of the quick, powerful and productive global optimization approach for programmed clustering. In our proposed approach, we additionally apply an entropy based method for distinguishing the concept drift in the data stream and in this way updating the clustering procedure online. We demonstrated that our proposed method is contrasted with Genetic Algorithm and identified as proficient optimization algorithm. The performance of our proposed technique is assessed and cr eates the accuracy of 92.29%, the precision is 86.96%, recall is 90.30% and F-measure estimate is 88.60%.
IRJET-Comparative Analysis of Apriori and Apriori with Hashing AlgorithmIRJET Journal
This document compares the Apriori and Apriori with hashing algorithms for association rule mining. Association rule mining is used to find frequent itemsets and discover relationships between items in transactional databases. The Apriori algorithm uses a bottom-up approach to generate frequent itemsets by joining candidate itemsets of length k with themselves. The Apriori with hashing algorithm improves efficiency by using a hash table to reduce the candidate itemset size. The document finds that Apriori with hashing outperforms the standard Apriori algorithm on large datasets by taking less time to generate frequent itemsets.
Experimental study of Data clustering using k- Means and modified algorithmsIJDKP
The k- Means clustering algorithm is an old algorithm that has been intensely researched owing to its ease
and simplicity of implementation. Clustering algorithm has a broad attraction and usefulness in
exploratory data analysis. This paper presents results of the experimental study of different approaches to
k- Means clustering, thereby comparing results on different datasets using Original k-Means and other
modified algorithms implemented using MATLAB R2009b. The results are calculated on some performance
measures such as no. of iterations, no. of points misclassified, accuracy, Silhouette validity index and
execution time
This document summarizes a research paper that proposes a dynamic approach to improving the k-means clustering algorithm. The proposed approach aims to address two weaknesses of the standard k-means algorithm: its requirement of prior knowledge of the number of clusters k, and its sensitivity to initialization. The approach determines initial cluster centroids by segmenting the data space and selecting high-frequency segments. It then uses the silhouette validity index to dynamically determine the optimal number of clusters k, rather than requiring the user to specify k. The approach is compared to the standard k-means algorithm and other modified approaches, and is shown to improve initial center selection and reduce computation time.
K-means Clustering Method for the Analysis of Log Dataidescitation
Clustering analysis method is one of the main
analytical methods in data mining; the method of clustering
algorithm will influence the clustering results directly. This
paper discusses the standard k-means clustering algorithm
and analyzes the shortcomings of standard k-means
algorithm. This paper also focuses on web usage mining to
analyze the data for pattern recognition. With the help of k-
means algorithm, pattern is identified.
Similar to 5 parallel implementation 06299286 (20)
2. B. Apriori Algorithm
The name of the Apriori algorithm is based on the fact
that the algorithm uses prior knowledge of frequent itemset
property which is that all nonempty subsets of a frequent
itemset must also be frequent [5]. The main idea is to find
the frequent itemsets.
The process of the algorithm is as follows.
Step1. Set the minimum support and confidence
according to the user definition.
Step2. Construct the candidate 1-itemsets. And then
generate the frequent 1-itemsets by pruning some candidate
1-itemsets if their support values are lower than the
minimum support.
Step3. Join the frequent 1-itemsets with each other to
construct the candidate 2-itemsets and prune some
infrequent itemsets from the candidate 2-itemsets to create
the frequent 2-itemsets.
Step4. Repeat the steps likewise step3 until no more
candidate itemsets can be created.
The main steps consist of join and prune actions and
the process is followed.
(1) The join step: To find kL , a set of candidate k
-itemsets is generated by joining ( 1)k − -itemsets. This set
of candidates is denoted as kC . Let 1l and 2l be itemsets
in 1kL − . The notation [ ]il j refers to the j th item in il .
The items within an itemset are sorted in lexicographic
order. The join 1kL − ڇ 1kL − , is performed, where members
1l and 2l of 1kL − are joinable if their first (k-2) items are in
common. The resulting itemsets formed by joining 1l and
2l is 1 1 1 1 2[1], [2], , [ 2], [ 1], [ 1].l l l k l k l k− − −!
The prune step: kC is a superset of kL , its member
may or may not be frequent. According to the Apriori
property, any ( 1)k − -itemsets that is not frequent cannot
be a subset of a frequent k -itemsets. Hence, if any subset
with length ( 1)k − of a candidate k -itemsets is not in
1kL − , then the candidate cannot be frequent either and so
can be removed from kC .
III. INTRODUCTION TO MAPREDUCE
MapReduce is a programming model and an associated
implementation for processing and generating large data
sets. As the framework showed in Figure1, MapReduce
specifies the computation in terms of a map and a reduce
function, and the underlying runtime system automatically
parallelizes the computation across large-scale clusters of
machines, handles machine failures, and schedules
inter-machine communication to make efficient use of the
network and disks.
Figure 1. Illustration of the MapReduce framework: the
“map” is applied to all input records, which generates
intermediate results that are aggregated by the “reduce”.
Map takes an input pair and produces a set of
intermediate key/value pairs. The MapReduce library
groups together all intermediate values associated with the
same intermediate key and passes them to the reduce
function [6]. That is, a map function is used to take a single
key/value pair and outputs a list of new key/value pairs. It
could be formalized as:
map :: (key1, value1) ֜ list(key2, value2)
The reduce function, also written by the user, accepts
an intermediate key and a set of values for that key. It
merges together these values to form a possibly smaller set
of values. The intermediate values are supplied to the users
reduce function via an iterator. This allows us to handle lists
of values that are too large to fit in memory. The reduce
function is given all associated values for the key and
outputs a new list of values. Mathematically, this could be
represented as:
reduce :: (key2, list(value2)) ֜ (key3, value3)
The MapReduce model provides sufficient high-level
parallelization. Since the map function only takes a single
record, all map operations are independent of each other
and fully parallelizable. Reduce function can be executed in
parallel on each set of intermediate pairs with the same key.
IV. PARALLEL APRIORI ALGORITHM BASED ON
MAPREDUCE
A. The main idea of the parallel Apriori algorithm
As described in Section2, the key step in Apriori
algorithm is to find the frequent itemsets. In the k th
iteration, it computes the occurrences of potential
candidates of size k in each of the transactions. It is
obviously that the occurrences counting of candidate
itemsets in one transaction is irrelevant with the counting in
another transaction in the same iteration. Therefore, the
occurrences computation process in one iteration could be
parallel executed. After this phase, all the occurrences of
candidate itemsets are summed up. Furthermore, the join
actions are done on the frequent k-itemsets and prune
237
3. actions are performed on the candidate (k+1)-itemsets.
Naturally, the frequent (k+1)-itemsets are found. Finally,
according to the frequent itemsets, the rules that have a
support and confidence greater than given thresholds are
generated.
Figure 2 shows the flow chart of parallel Apriori
algorithm, which is denoted as PApriori. The steps are as
follows.
Step1. Use MapReduce model to find the frequent
1-itemsets.
Step2. Set 1k = .
Step3.If the frequent (k+1)-itemsets cannot be generated,
then goto Step6.
Step4. According to the frequent k -itemsets, use
MapReduce model to generate the frequent
(k+1)-itemsets.
Step5. If k is less than the max iteration times, then
k + + , goto Step3; Otherwise, continue to the next
step.
Step6. According to the frequent itemsets L , generate the
strong rules.
k kL*
Figure 2. The flow chart of the parallel Apriori algorithm
B. The parallel implementation of the Apriori algorithm
based on MapReduce
As the analysis mentioned above, PApriori algorithm
needs one kind of MapReduce job. The map function
performs the procedure of counting each occurrence of
potential candidates of size k and thus the map stage
realizes the occurrences counting for all the potential
candidates in a parallel way. Then, the reduce function
performs the procedure of summing the occurrences counts.
For each round of the iteration, such a job is carried out to
implement the occurrences computing for potential
candidates of size k.
Map-function The input dataset is stored on HDFS[1]
as a sequence file of key, value pairs, each of which
represents a record in the dataset. The key is the offset in
bytes of this record to the start point of the data file, and the
value is a string of the content of this record. The dataset is
splitted and globally broadcasted to all mappers.
Consequently, the occurrence computations are parallel
executed. For each map task, once the items in the
candidate itemsets occur in the transactions, the key’, 1
pair will be outputted, where key’ is the candidate itemsets.
We use m_cycles to represent the maximum cycles of the
PApriori. The pseudo-code of map function is shown in
Algorithm 1.
Algorithm1. Map(key, value)
Input: Global variable m_cycles, the offset key, the sample
value
Output: key’, value’ pair, where the key’ is the candidate
itemsets and value’ is the once occurrence of the key’,
actually, it equals to 1.
1. if (m_cycles1) /*for the case k1*/
2. For each itemset kiC in the candidate
k -itemsets
3. If kiC
is a subset of value
4. Output( kiC , 1);
5. Endif
6. End For
7. Else For each itemset iI in value /*k=1*/
8. If 0iI ≠
9. Output( iI ,1);
10. Endif
11. End For
Reduce-function The input of the reduce function is
the data obtained from the map function of each host. In
reduce step, we sum up all the values with the same key and
get the final result. In another word, we can get the total
occurrences of potential candidates in the transactions. The
pseudo-code for reduce function is shown in Algorithm2.
Algorithm2. Reduce(key, Value)
Input: key is the element of the candidate itemsets, Value
is once occurrence of the key
Output: key’, value’ pair, where the key’ is identical to
key and value’ is total occurrence of the key’.
1. sum=0;
2. while( values.hasNext()){
3. sum+=values.next();
4. }
5.output (key, sum);
V. EXPERIMENTAL RESULTS
In this section, we evaluate the performance of our
238
4. proposed PApriori algorithm in terms of sizeup, speedup
and scaleup to deal with large scale dataset.
A. The datasets
The transactional data for an AllElectronics branch and
T10I4D100K dataset are used in our experiments. As
shown in Table1, there are nine transactions in the
transactional data. We denote it as dataset1and replicate it
to get 1GB, 2GB, 4GB, and 8GB datasets respectively.
They have many short transactions with few frequent
itemsets. For the T10I4D100K dataset, we replicate it to 2
times, 4 times, 8 times and get 0.6G, 1.2G, 2.4G datasets,
we denote them as T10I4D200K, T10I4D400K and
T10I4D800K respectively. They have fewer larger
transactions with many frequent itemsets. Performance
experiments were run on a cluster of 10 computers, six with
four 2.8GHz cores and 4GB memory, the rest four with two
2.8GHz cores and 4GB memory. Hadoop version 0.20.2
and Java 1.5.0_14 are used as the MapReduce system for all
the experiments. Experiments were carried on 10 times to
obtain stable values for each data point.
Table1. TRANSACTIONAL DATA FOR AN ALLELECTRONICS BRANCH
TID List of item_IDs
T100 I1,I2,I5
T200 I2,I4
T300 I2,I3
T400 I1,I2,I4
T500 I1,I3
T600 I2,I3
T700 I1,I3
T800 I1,I2,I3,I5
T900 I1,I2,I3
B. The evaluation measure
We use scaleup, sizeup and speedup to evaluate the
performance of PApriori algorithm.
Scaleup: Scaleup evaluates the ability of the algorithm
to grow both the system and the dataset size. Scaleup is
defined as the ability of an m-times larger system to
perform an m-times larger job in the same run-time as the
original system. The definition is as follows.
1
( , )
mm
T
Scaleup data m
T
= (1)
Where, 1T is the execution time for processing data on
1 core, mmT is the execution time for processing m*data
on m cores.
Sizeup: Sizeup analysis holds the number of cores in
the system constant, and grows the size of the datasets by
the factor m. Sizeup measures how much longer it takes on
a given system, when the dataset size is m-times larger than
the original dataset. It is defined by the following formula:
1
( , ) mT
Sizeup data m
T
= (2)
Where, mT is the execution time for processing
m*data, 1T is the execution time for processing data.
Speedup: Speedup refers to how much a parallel
algorithm is faster than a corresponding sequential
algorithm. It is defined by the following formula:
1
p
T
Speedup
T
= (3)
Where, p is the number of processors, 1T is the
execution time of the algorithm with one processor, pT is
the execution time of the parallel algorithm with p
processors.
Linear speedup or ideal speedup is obtained when
Speedup p= . When running an algorithm with linear
speedup, doubling the number of processors doubles the
speed. In practice, linear speedup is difficult to achieve
because the communication cost increases with the number
of records becomes large.
C. The Performance and Analysis
We examine the scaleup, sizeup and speedup
characteristics of the PApriori algorithm.
To demonstrate how well the PApriori algorithm
handles larger datasets when more cores of computers are
available, we have performed scaleup experiments where
we have increased the size of the datasets in direct
proportion to the number of cores in the system. For
dataset1, the datasets size of 1GB, 2GB, 4GB and 8GB are
executed on 4, 8, 16 and 32 cores respectively. For dataset
T10I4D100K, T10I4D100K, T10I4D4200K, T10I4D400K
and T10I4D1800K are executed in the same way.
Figure3 shows the scaleup performance of the datasets.
Clearly, the PApriori algorithm scales well, the scaleup fall
shortly as the database and multiprocessor sizes increase. It
always maintains a higher than 78% scalability for dataset1
and 80% for T10I4D100K.
To measure the performance of sizeup, we fix the
number of cores to 4, 8, 16 and 32 respectively. Figure 4
shows the sizeup results on different cores. When the
number of cores is small such as 4 and 8, the sizeup
performances differ little. However, as more cores are
available, the sizeup value for 16 or 32 cores decreases
significantly compared to that of 4 or 8 cores on the same
data sets. The results show sublinear performance for the
PApriori algorithm, the program is actually more efficient
as the database size is increased. Increasing the size of the
dataset simply makes the noncommunication portion of the
code take more time due to more I/O and more transaction
processing. This has the result of reducing the percentage of
the overall time spent in communication. Since I/O and
CPU processing scale well with sizeup, we get sublinear
performance.
To measure the speedup, we kept the dataset constant
239
5. and varied the number of cores. The number of cores varies
from 4 to 32. We have performed four experiments, the size
of the dataset increases from 1GB to 8GB for dataset1, and
from 0.3GB to 2.4GB for T10I4D100K.
We have performed the speedup evaluation on datasets
with different sizes and systems. Figure 5 shows the
speedup for different datasets. As the result shows, the
speedup performance does however not to be very good in
the case of 1GB for dataset1 and 0.3GB for T10I4D100K.
This is an artifact of the small amount of data each node
processing. In this case, communication cost becomes a
significant percentage of the overall response time. This is
easily predicted from our sizeup experiments where we
notice that the more data a core processes, the less
significant becomes the communication cost giving us
better performance. Therefore, PApriori algorithm can deal
with large datasets efficiently. Larger datasets would have
shown even better speedup characteristics.
(a) Scaleup for dataset1
(b) Scaleup for T10I4D100K
Figure 3. Scaleup performance evaluation
(a) Sizeup for dataset1
(b) Sizeup for T10I4D100K
Figure 4. Sizeup performance evaluation
(a) Speedup for dataset1
(b) Speedup for T10I4D100K
Figure 5. Speedup performance evaluation
To sum up, for the datasets either have many short
transactions with few frequent itemsets or fewer larger
transactions with many frequent itemsets, PApriori
algorithm has shown good performance.
VI. CONCLUSION
Searching for frequent patterns in transactional
databases is considered one of the most important data
mining problems. The task of finding all association rules
requires a lot of computation power and memory. In this
paper, we propose a fast parallel Apriori algorithm based on
MapReduce, We use sizeup, speedup and scaleup to
evaluate the performances of PApriori. The experimental
results show that the program is actually more efficient as
the database size is increased. Therefore, the proposed
algorithm can process large datasets on commodity
240
6. hardware effectively.
ACKNOWLEDGMENTS
This work is Supported by the National Natural
Science Foundation of China (No. 60933004, 60975039,
61175052, 61035003, 61072085), National High-tech RD
Program of China (863 Program) (No.2012AA011003).
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