HFIM: a Spark-based hybrid frequent itemset mining algorithm for big data processing

Frequent itemset mining is one of the data mining techniques applied to discover frequent patterns, used in prediction, association rule mining, classification, etc. Apriori algorithm is an iterative algorithm, which is used to find frequent itemsets from transactional dataset. It scans complete dat...

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Published in:	The Journal of supercomputing Vol. 73; no. 8; pp. 3652 - 3668
Main Authors:	Sethi, Krishan Kumar, Ramesh, Dharavath
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.08.2017 Springer Nature B.V
Subjects:	Algorithms Big Data Compilers Computer Science Data management Data mining Data processing Datasets Interpreters Iterative algorithms Processor Architectures Programming Languages Scanning Frequent pattern mining Apriori algorithm Big data Apache Spark
ISSN:	0920-8542, 1573-0484
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Abstract	Frequent itemset mining is one of the data mining techniques applied to discover frequent patterns, used in prediction, association rule mining, classification, etc. Apriori algorithm is an iterative algorithm, which is used to find frequent itemsets from transactional dataset. It scans complete dataset in each iteration to generate the large frequent itemsets of different cardinality, which seems better for small data but not feasible for big data. The MapReduce framework provides the distributed environment to run the Apriori on big transactional data. However, MapReduce is not suitable for iterative process and declines the performance. We introduce a novel algorithm named Hybrid Frequent Itemset Mining (HFIM), which utilizes the vertical layout of dataset to solve the problem of scanning the dataset in each iteration. Vertical dataset carries information to find support of each itemsets. Moreover, we also include some enhancements to reduce number of candidate itemsets. The proposed algorithm is implemented over Spark framework, which incorporates the concept of resilient distributed datasets and performs in-memory processing to optimize the execution time of operation. We compare the performance of HFIM with another Spark-based implementation of Apriori algorithm for various datasets. Experimental results show that the HFIM performs better in terms of execution time and space consumption.
AbstractList	Frequent itemset mining is one of the data mining techniques applied to discover frequent patterns, used in prediction, association rule mining, classification, etc. Apriori algorithm is an iterative algorithm, which is used to find frequent itemsets from transactional dataset. It scans complete dataset in each iteration to generate the large frequent itemsets of different cardinality, which seems better for small data but not feasible for big data. The MapReduce framework provides the distributed environment to run the Apriori on big transactional data. However, MapReduce is not suitable for iterative process and declines the performance. We introduce a novel algorithm named Hybrid Frequent Itemset Mining (HFIM), which utilizes the vertical layout of dataset to solve the problem of scanning the dataset in each iteration. Vertical dataset carries information to find support of each itemsets. Moreover, we also include some enhancements to reduce number of candidate itemsets. The proposed algorithm is implemented over Spark framework, which incorporates the concept of resilient distributed datasets and performs in-memory processing to optimize the execution time of operation. We compare the performance of HFIM with another Spark-based implementation of Apriori algorithm for various datasets. Experimental results show that the HFIM performs better in terms of execution time and space consumption.
Author	Ramesh, Dharavath Sethi, Krishan Kumar
Author_xml	– sequence: 1 givenname: Krishan Kumar surname: Sethi fullname: Sethi, Krishan Kumar organization: Department of Computer Science and Engineering, Indian Institute of Technology (ISM) – sequence: 2 givenname: Dharavath orcidid: 0000-0003-3338-6520 surname: Ramesh fullname: Ramesh, Dharavath email: ramesh.d.in@ieee.org organization: Department of Computer Science and Engineering, Indian Institute of Technology (ISM)
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SubjectTerms	Algorithms Big Data Compilers Computer Science Data management Data mining Data processing Datasets Interpreters Iterative algorithms Processor Architectures Programming Languages Scanning
Title	HFIM: a Spark-based hybrid frequent itemset mining algorithm for big data processing
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