Multi-objective genetic-fuzzy data mining

Chun Hao Chen, Tzung Pei Hong*, S. Tseng, Lien Chin Chen

*Corresponding author for this work

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

Many approaches have been proposed for mining fuzzy association rules. The membership functions, which critically influence the final mining results, are difficult to define. In general, multiple criteria are considered when defining membership functions. In this paper, a multi-objective genetic-fuzzy mining algorithm is proposed for extracting membership functions and association rules from quantitative transactions. Two objective functions are used to find the Pareto front. The first one is the suitability of membership functions. It consists of the coverage factor and the overlap factor and is used to avoid two unsuitable types of membership function. The second one is the total number of large 1-itemsets from a given set of minimum support values. Experimental results show the effectiveness of the proposed approach in finding the Pareto-front membership functions.

Original languageEnglish
Pages (from-to)6551-6568
Number of pages18
JournalInternational Journal of Innovative Computing, Information and Control
Volume8
Issue number10 A
StatePublished - 1 Oct 2012

Keywords

  • Data mining
  • Fuzzy association rules
  • Fuzzy set
  • Genetic algorithm
  • Multi-objective optimization

Fingerprint Dive into the research topics of 'Multi-objective genetic-fuzzy data mining'. Together they form a unique fingerprint.

  • Cite this