Volume 6 Number 1 (Jan. 2011)
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JSW 2011 Vol.6(1): 91-99 ISSN: 1796-217X
doi: 10.4304/jsw.6.1.91-99

ECOGA: Efficient Data Mining Approach for Fuzzy Association Rules

Wanneng Shu1, 2, Lixin Ding1, 3
1State key lab of software engineering, Wuhan University Wuhan 430072, China
2College of Computer Science, South-Central University for Nationalities Wuhan 430074, China
3Computer School, Wuhan University, Wuhan, 430072, China


Abstract—Data mining is concerned with developing algorithms and computational tools and techniques to help people extract patterns from data. In this paper an efficient data mining approach, which is based on fuzzy set theory and clonal selection algorithm, is proposed. The main motivation is to benefit from the global search performed by this kind of algorithms. Experimental results show the number of fuzzy association rules obtained with the proposed method is larger than those obtained by applying other methods.

Index Terms—data mining, association rules, fuzzy sets, efficient clonal optimizing genetic algorithm

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Cite: Wanneng Shu, Lixin Ding, "ECOGA: Efficient Data Mining Approach for Fuzzy Association Rules," Journal of Software vol. 6, no. 1, pp. 91-99, 2011.

General Information

ISSN: 1796-217X (Online)
Frequency: Monthly (2006-2019); Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, Google Scholar, ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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