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article

CBAR: an efficient method for mining association rules

Authors:

Yuh-Jiuan Tsay,

Jiunn-Yann ChiangAuthors Info & Claims

Knowledge-Based Systems, Volume 18, Issue 2-3

Pages 99 - 105

https://doi.org/10.1016/j.knosys.2004.04.010

Published: 01 April 2005 Publication History

Abstract

The discovery of association rules is an important data-mining task for which many algorithms have been proposed. However, the efficiency of these algorithms needs to be improved to handle real-world large datasets. In this paper, we present an efficient algorithm named cluster-based association rule (CBAR). The CBAR method is to create cluster tables by scanning the database once, and then clustering the transaction records to the k-th cluster table, where the length of a record is k. Moreover, the large itemsets are generated by contrasts with the partial cluster tables. This not only prunes considerable amounts of data reducing the time needed to perform data scans and requiring less contrast, but also ensures the correctness of the mined results. Experiments with the FoodMart transaction database provided by Microsoft SQL Server show that CBAR outperforms Apriori, a well-known and widely used association rule.

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Cited By

Haitao S(2020)Big data analysis of e-commerce loan risk of college students in the context of network financeInformation Systems and e-Business Management10.1007/s10257-019-00424-918:3(439-454)Online publication date: 1-Sep-2020
https://dl.acm.org/doi/10.1007/s10257-019-00424-9
Ansari ESadreddini MMirsadeghi SKeshtkaran MWallace R(2018)TFI-AprioriIntelligent Data Analysis10.3233/IDA-17347322:4(807-827)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.3233/IDA-173473
Suh YPark Y(2018)Identifying and structuring service functions of mobile applications in Google's Android MarketInformation Systems and e-Business Management10.1007/s10257-017-0366-716:2(383-406)Online publication date: 1-May-2018
https://dl.acm.org/doi/10.1007/s10257-017-0366-7
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cover image Knowledge-Based Systems

Knowledge-Based Systems Volume 18, Issue 2-3

April, 2005

57 pages

ISSN:0950-7051

Issue’s Table of Contents

Copyright © Elsevier B.V. © 2004.

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Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 April 2005

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Cited By

Haitao S(2020)Big data analysis of e-commerce loan risk of college students in the context of network financeInformation Systems and e-Business Management10.1007/s10257-019-00424-918:3(439-454)Online publication date: 1-Sep-2020
https://dl.acm.org/doi/10.1007/s10257-019-00424-9
Ansari ESadreddini MMirsadeghi SKeshtkaran MWallace R(2018)TFI-AprioriIntelligent Data Analysis10.3233/IDA-17347322:4(807-827)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.3233/IDA-173473
Suh YPark Y(2018)Identifying and structuring service functions of mobile applications in Google's Android MarketInformation Systems and e-Business Management10.1007/s10257-017-0366-716:2(383-406)Online publication date: 1-May-2018
https://dl.acm.org/doi/10.1007/s10257-017-0366-7
Suchacka GChodak G(2017)Using association rules to assess purchase probability in online storesInformation Systems and e-Business Management10.1007/s10257-016-0329-415:3(751-780)Online publication date: 1-Aug-2017
https://dl.acm.org/doi/10.1007/s10257-016-0329-4
Elloumi SFerjani FJaoua A(2016)Using minimal generators for composite isolated point extraction and conceptual binary relation coverageInformation Sciences: an International Journal10.1016/j.ins.2015.12.013336:C(129-144)Online publication date: 1-Apr-2016
https://dl.acm.org/doi/10.1016/j.ins.2015.12.013
Dam TLi KFournier-Viger PDuong Q(2016)An efficient algorithm for mining top-rank-k frequent patternsApplied Intelligence10.1007/s10489-015-0748-945:1(96-111)Online publication date: 1-Jul-2016
https://dl.acm.org/doi/10.1007/s10489-015-0748-9
Lozano MRodriguez FGarcía-Martínez C(2015)A two-stage constructive method for the unweighted minimum string cover problemKnowledge-Based Systems10.1016/j.knosys.2015.01.00377:C(103-113)Online publication date: 1-Mar-2015
https://dl.acm.org/doi/10.1016/j.knosys.2015.01.003
Ben Ammar AAbdellatif ABen Ghezala HCamacho DAkerkar RRodriguez Moreno M(2013)MOWSProceedings of the 3rd International Conference on Web Intelligence, Mining and Semantics10.1145/2479787.2479790(1-10)Online publication date: 12-Jun-2013
https://dl.acm.org/doi/10.1145/2479787.2479790
Chen CChiang RLee CChen C(2012)Improving the performance of association classifiers by rule prioritizationKnowledge-Based Systems10.1016/j.knosys.2012.06.00436(59-67)Online publication date: 1-Dec-2012
https://dl.acm.org/doi/10.1016/j.knosys.2012.06.004
Mohamed MDarwieesh MAli A(2011)Advanced Matrix Algorithm (AMA): reducing number of scans for association rule generationInternational Journal of Business Intelligence and Data Mining10.1504/IJBIDM.2011.0394126:2(202-214)Online publication date: 1-Apr-2011
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