article

Mining changes in association rules: a fuzzy approach

Authors:

Keith C. C. ChanAuthors Info & Claims

Fuzzy Sets and Systems, Volume 149, Issue 1

Pages 87 - 104

https://doi.org/10.1016/j.fss.2004.07.018

Published: 01 January 2005 Publication History

Abstract

Association rule mining is concerned with the discovery of interesting association relationships hidden in databases. Existing algorithms typically assume that data characteristics are stable over time. Their main focus is therefore to mine association rules in an efficient manner. However, the world constantly changes. This makes the characteristics of real-life entities represented by the data and hence the associations hidden in the data change over time. Detecting and adapting to the changes are usually critical to the success of many business organizations. This paper presents the problem of mining changes in association rules. Given a set of database partitions, each of which contains a set of transactions collected in a specific time period, a set of association rules is discovered in each database partition. We propose to perform data mining in the discovered association rules so as to reveal the regularities governing how the rules change in different time periods. Since the nature of many real-life entities is rather fuzzy, we propose to use linguistic variables and linguistic terms to represent the changes in the discovered association rules. In particular, fuzzy decision trees are built to discover the changes in the discovered association rules. The fuzzy decision trees are then converted to a set of fuzzy rules, called fuzzy meta-rules because they are rules about rules. By doing so, the changes hidden in the data can be revealed and presented to human users in a comprehensible form. Furthermore, the discovered changes can also be used to predict any change in the future. To evaluate the performance of our approach, we make use of a set of synthetic datasets, which are database partitions collected in different time periods. A set of association rules is discovered in each dataset. Fuzzy decision trees are constructed in the discovered association rules in order to reveal the changes in these rules. The experimental results show that our approach is very promising.

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Cited By

Ruiz MGómez-Romero JMolina-Solana MRos MMartin-Bautista M(2017)Information fusion from multiple databases using meta-association rulesInternational Journal of Approximate Reasoning10.1016/j.ijar.2016.09.00680:C(185-198)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1016/j.ijar.2016.09.006
Huang THuang CChuang Y(2016)Change discovery of learning performance in dynamic educational environmentsTelematics and Informatics10.1016/j.tele.2015.10.00533:3(773-792)Online publication date: 1-Aug-2016
https://dl.acm.org/doi/10.1016/j.tele.2015.10.005
Ruiz MGmez-Romero JMolina-Solana MCampaa JMartin-Bautista M(2016)Meta-association rules for mining interesting associations in multiple datasetsApplied Soft Computing10.1016/j.asoc.2016.08.01449:C(212-223)Online publication date: 1-Dec-2016
https://dl.acm.org/doi/10.1016/j.asoc.2016.08.014
Show More Cited By

Mining changes in association rules: a fuzzy approach
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
2. Information systems
  1. Information systems applications

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Copyright © Elsevier B.V. © 2004.

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Elsevier North-Holland, Inc.

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Published: 01 January 2005

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Ruiz MGómez-Romero JMolina-Solana MRos MMartin-Bautista M(2017)Information fusion from multiple databases using meta-association rulesInternational Journal of Approximate Reasoning10.1016/j.ijar.2016.09.00680:C(185-198)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1016/j.ijar.2016.09.006
Huang THuang CChuang Y(2016)Change discovery of learning performance in dynamic educational environmentsTelematics and Informatics10.1016/j.tele.2015.10.00533:3(773-792)Online publication date: 1-Aug-2016
https://dl.acm.org/doi/10.1016/j.tele.2015.10.005
Ruiz MGmez-Romero JMolina-Solana MCampaa JMartin-Bautista M(2016)Meta-association rules for mining interesting associations in multiple datasetsApplied Soft Computing10.1016/j.asoc.2016.08.01449:C(212-223)Online publication date: 1-Dec-2016
https://dl.acm.org/doi/10.1016/j.asoc.2016.08.014
Yang HYang C(2015)Using Health-Consumer-Contributed Data to Detect Adverse Drug Reactions by Association Mining with Temporal AnalysisACM Transactions on Intelligent Systems and Technology10.1145/27004826:4(1-27)Online publication date: 13-Jul-2015
https://dl.acm.org/doi/10.1145/2700482
Cariñena P(2014)Fuzzy temporal association rulesWiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery10.1002/widm.11164:1(64-70)Online publication date: 1-Jan-2014
https://dl.acm.org/doi/10.1002/widm.1116
Ye FLiu JQian JShi Y(2011)Discovering association rules change from large databasesProceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part I10.5555/2045625.2045682(388-395)Online publication date: 24-Sep-2011
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Shen BYao MWu ZGao Y(2010)Mining dynamic association rules with commentsKnowledge and Information Systems10.5555/3225669.322601423:1(73-98)Online publication date: 1-Apr-2010
https://dl.acm.org/doi/10.5555/3225669.3226014
Koopaei MMinaei-Bidgoli BChau PLyytinen KWei CYang CLin F(2009)A new method for ranking changes in customer's behavioral patterns in department storesProceedings of the 11th International Conference on Electronic Commerce10.1145/1593254.1593303(317-322)Online publication date: 12-Aug-2009
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Qin YZhang SZhu XZhang JZhang C(2009)Estimating confidence intervals for structural differences between contrast groups with missing dataExpert Systems with Applications: An International Journal10.1016/j.eswa.2008.07.06836:3(6431-6438)Online publication date: 1-Apr-2009
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Böttcher MSpott MNauck D(2007)A framework for discovering and analyzing changing customer segmentsProceedings of the 7th industrial conference on Advances in data mining: theoretical aspects and applications10.5555/1770770.1770796(255-268)Online publication date: 14-Jul-2007
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