Finding pareto-front membership functions in fuzzy data mining

Chun Hao Chen, Tzung Pei Hong*, Vincent Shin-Mu Tseng

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


Transactions with quantitative values are commonly seen in real-world applications. Fuzzy mining algorithms have thus been developed recently to induce linguistic knowledge from quantitative databases. In fuzzy data mining, the membership functions have a critical influence on the final mining results. How to effectively decide the membership functions in fuzzy data mining thus becomes very important. In the past, we proposed a fuzzy mining approach based on the Multi-Objective Genetic Algorithm (MOGA) to find the Pareto front of the desired membership functions. In this paper, we adopt a more sophisticated multi-objective approach, the SPEA2, to find the appropriate sets of membership functions for fuzzy data mining. Two objective functions are used to find the Pareto front. The first one is the suitability of membership functions and the second one is the total number of large 1-itemsets derived. Experimental comparisons of the proposed and the previous approaches are also made to show the effectiveness of the proposed approach in finding the Pareto-front membership functions.

Original languageEnglish
Pages (from-to)343-354
Number of pages12
JournalInternational Journal of Computational Intelligence Systems
Issue number2
StatePublished - 1 Jan 2012


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

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