article

Mining frequent itemsets in large databases: The hierarchical partitioning approach

Author:

Fan-Chen TsengAuthors Info & Claims

Expert Systems with Applications: An International Journal, Volume 40, Issue 5

Pages 1654 - 1661

https://doi.org/10.1016/j.eswa.2012.09.005

Published: 01 April 2013 Publication History

Abstract

Although many methods have been proposed to enhance the efficiencies of data mining, little research has been devoted to the issue of scalability - that is, the problem of mining frequent itemsets when the size of the database is very large. This study proposes a methodology, hierarchical partitioning, for mining frequent itemsets in large databases, based on a novel data structure called the Frequent Pattern List (FPL). One of the major features of the FPL is its ability to partition the database, and thus transform the database into a set of sub-databases of manageable sizes. As a result, a divide-and-conquer approach can be developed to perform the desired data-mining tasks. Experimental results show that hierarchical partitioning is capable of mining frequent itemsets and frequent closed itemsets in very large databases.

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Cited By

Xu WLu J(2023)Bit Splicing Frequent Itemset Mining Algorithm Based on Dynamic GroupingWeb and Big Data10.1007/978-981-97-2387-4_28(417-432)Online publication date: 6-Oct-2023
https://dl.acm.org/doi/10.1007/978-981-97-2387-4_28
Yadav DChowdary C(2018)OOIMASPExpert Systems with Applications: An International Journal10.1016/j.eswa.2017.10.01593:C(62-71)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1016/j.eswa.2017.10.015
Ryang HYun URyu K(2016)Fast algorithm for high utility pattern mining with the sum of item quantitiesIntelligent Data Analysis10.3233/IDA-16081120:2(395-415)Online publication date: 1-Jan-2016
https://dl.acm.org/doi/10.3233/IDA-160811
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Mining frequent itemsets in large databases: The hierarchical partitioning approach
1. Computing methodologies
2. Information systems
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cover image Expert Systems with Applications: An International Journal

Expert Systems with Applications: An International Journal Volume 40, Issue 5

April, 2013

505 pages

ISSN:0957-4174

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Copyright © Elsevier Ltd © 2012.

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Pergamon Press, Inc.

United States

Publication History

Published: 01 April 2013

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Cited By

Xu WLu J(2023)Bit Splicing Frequent Itemset Mining Algorithm Based on Dynamic GroupingWeb and Big Data10.1007/978-981-97-2387-4_28(417-432)Online publication date: 6-Oct-2023
https://dl.acm.org/doi/10.1007/978-981-97-2387-4_28
Yadav DChowdary C(2018)OOIMASPExpert Systems with Applications: An International Journal10.1016/j.eswa.2017.10.01593:C(62-71)Online publication date: 1-Mar-2018
https://dl.acm.org/doi/10.1016/j.eswa.2017.10.015
Ryang HYun URyu K(2016)Fast algorithm for high utility pattern mining with the sum of item quantitiesIntelligent Data Analysis10.3233/IDA-16081120:2(395-415)Online publication date: 1-Jan-2016
https://dl.acm.org/doi/10.3233/IDA-160811
Ryang HYun U(2016)High utility pattern mining over data streams with sliding window techniqueExpert Systems with Applications: An International Journal10.1016/j.eswa.2016.03.00157:C(214-231)Online publication date: 15-Sep-2016
https://dl.acm.org/doi/10.1016/j.eswa.2016.03.001
Tseng FTeng C(2015)Online Gamers' Preferences for Online Game Charging MechanismsInternational Journal of E-Business Research10.4018/ijebr.201501010211:1(23-34)Online publication date: 1-Jan-2015
https://dl.acm.org/doi/10.4018/ijebr.2015010102
Soysal Ö(2015)Association rule mining with mostly associated sequential patternsExpert Systems with Applications: An International Journal10.1016/j.eswa.2014.10.04942:5(2582-2592)Online publication date: 1-Apr-2015
https://dl.acm.org/doi/10.1016/j.eswa.2014.10.049

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