An improved approach to find membership functions and multiple minimum supports in fuzzy data mining

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

*Corresponding author for this work

Research output: Contribution to journalArticle

21 Scopus citations

Abstract

Fuzzy mining approaches have recently been discussed for deriving fuzzy knowledge. Since items may have their own characteristics, different minimum supports and membership functions may be specified for different items. In the past, we proposed a genetic-fuzzy data-mining algorithm for extracting minimum supports and membership functions for items from quantitative transactions. In that paper, minimum supports and membership functions of all items are encoded in a chromosome such that it may be not easy to converge. In this paper, an enhanced approach is proposed, which processes the items in a divide-and-conquer strategy. The approach is called divide-and-conquer genetic-fuzzy mining algorithm for items with Multiple Minimum Supports (DGFMMS), and is designed for finding minimum supports, membership functions, and fuzzy association rules. Possible solutions are evaluated by their requirement satisfaction divided by their suitability of derived membership functions. The proposed GA framework maintains multiple populations, each for one item's minimum support and membership functions. The final best minimum supports and membership functions in all the populations are then gathered together to be used for mining fuzzy association rules. Experimental results also show the effectiveness of the proposed approach.

Original languageEnglish
Pages (from-to)10016-10024
Number of pages9
JournalExpert Systems with Applications
Volume36
Issue number6
DOIs
StatePublished - 1 Aug 2009

Keywords

  • Data mining
  • Fuzzy set
  • Genetic algorithm
  • Genetic-fuzzy mining
  • Membership functions
  • Multiple minimum supports

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