Right-Hand Side Expanding Algorithm for Maximal Frequent Itemset Mining

When it comes to association rule mining, all frequent itemsets are first found, and then the confidence level of association rules is calculated through the support degree of frequent itemsets. As all non-empty subsets in frequent itemsets are still frequent itemsets, all frequent itemsets can be a...

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Autores principales:	Yalong Zhang, Wei Yu, Qiuqin Zhu, Xuan Ma, Hisakazu Ogura
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	association rule frequent itemset mining big data maximal frequent itemsets Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/7fc973f5996244e0b414927259731e4e
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id	oai:doaj.org-article:7fc973f5996244e0b414927259731e4e
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spelling	oai:doaj.org-article:7fc973f5996244e0b414927259731e4e2021-11-11T15:23:59ZRight-Hand Side Expanding Algorithm for Maximal Frequent Itemset Mining10.3390/app1121103992076-3417https://doaj.org/article/7fc973f5996244e0b414927259731e4e2021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10399https://doaj.org/toc/2076-3417When it comes to association rule mining, all frequent itemsets are first found, and then the confidence level of association rules is calculated through the support degree of frequent itemsets. As all non-empty subsets in frequent itemsets are still frequent itemsets, all frequent itemsets can be acquired only by finding all maximal frequent itemsets (MFIs), whose supersets are not frequent itemsets. In this study, an algorithm, named <i>right-hand side expanding</i> (RHSE), which can accurately find all MFIs, was proposed. First, an Expanding Operation was designed, which, starting from any given frequent itemset, could add items using certain rules and form some supersets of given frequent itemsets. In addition, these supersets were all MFIs. Next, this operator was used to add items by taking all frequent 1-itemsets as the starting point alternately, and all MFIs were found in the end. Due to the special design of the Expanding Operation, each MFI could be found. Moreover, the path found was unique, which avoided the algorithm redundancy in temporal and spatial complexity. This algorithm, which has a high operating rate, is applicable to the big data of high-dimensional mass transactions as it is capable of avoiding the computing redundancy and finding all MFIs. In the end, a detailed experimental report on 10 open standard transaction sets was given in this study, including the big data calculation results of million-class transactions.Yalong ZhangWei YuQiuqin ZhuXuan MaHisakazu OguraMDPI AGarticleassociation rulefrequent itemset miningbig datamaximal frequent itemsetsTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10399, p 10399 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	association rule frequent itemset mining big data maximal frequent itemsets Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	association rule frequent itemset mining big data maximal frequent itemsets Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Yalong Zhang Wei Yu Qiuqin Zhu Xuan Ma Hisakazu Ogura Right-Hand Side Expanding Algorithm for Maximal Frequent Itemset Mining
description	When it comes to association rule mining, all frequent itemsets are first found, and then the confidence level of association rules is calculated through the support degree of frequent itemsets. As all non-empty subsets in frequent itemsets are still frequent itemsets, all frequent itemsets can be acquired only by finding all maximal frequent itemsets (MFIs), whose supersets are not frequent itemsets. In this study, an algorithm, named <i>right-hand side expanding</i> (RHSE), which can accurately find all MFIs, was proposed. First, an Expanding Operation was designed, which, starting from any given frequent itemset, could add items using certain rules and form some supersets of given frequent itemsets. In addition, these supersets were all MFIs. Next, this operator was used to add items by taking all frequent 1-itemsets as the starting point alternately, and all MFIs were found in the end. Due to the special design of the Expanding Operation, each MFI could be found. Moreover, the path found was unique, which avoided the algorithm redundancy in temporal and spatial complexity. This algorithm, which has a high operating rate, is applicable to the big data of high-dimensional mass transactions as it is capable of avoiding the computing redundancy and finding all MFIs. In the end, a detailed experimental report on 10 open standard transaction sets was given in this study, including the big data calculation results of million-class transactions.
format	article
author	Yalong Zhang Wei Yu Qiuqin Zhu Xuan Ma Hisakazu Ogura
author_facet	Yalong Zhang Wei Yu Qiuqin Zhu Xuan Ma Hisakazu Ogura
author_sort	Yalong Zhang
title	Right-Hand Side Expanding Algorithm for Maximal Frequent Itemset Mining
title_short	Right-Hand Side Expanding Algorithm for Maximal Frequent Itemset Mining
title_full	Right-Hand Side Expanding Algorithm for Maximal Frequent Itemset Mining
title_fullStr	Right-Hand Side Expanding Algorithm for Maximal Frequent Itemset Mining
title_full_unstemmed	Right-Hand Side Expanding Algorithm for Maximal Frequent Itemset Mining
title_sort	right-hand side expanding algorithm for maximal frequent itemset mining
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/7fc973f5996244e0b414927259731e4e
work_keys_str_mv	AT yalongzhang righthandsideexpandingalgorithmformaximalfrequentitemsetmining AT weiyu righthandsideexpandingalgorithmformaximalfrequentitemsetmining AT qiuqinzhu righthandsideexpandingalgorithmformaximalfrequentitemsetmining AT xuanma righthandsideexpandingalgorithmformaximalfrequentitemsetmining AT hisakazuogura righthandsideexpandingalgorithmformaximalfrequentitemsetmining
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Right-Hand Side Expanding Algorithm for Maximal Frequent Itemset Mining

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