Cluster-based evaluation in fuzzy-genetic data mining

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

*Corresponding author for this work

Research output: Contribution to journalArticle

36 Scopus citations


Data mining is commonly used in attempts to induce association rules from transaction data. Most previous studies focused on binary-valued transaction data. Transactions in real-world applications, however, usually consist of quantitative values. In the past, we proposed a fuzzy-genetic data-mining algorithm for extracting both association rules and membership functions from quantitative transactions. It used a combination of large 1-itemsets and membership-function suitability to evaluate the fitness values of chromosomes. The calculation for large 1-itemsets could take a lot of time, especially when the database to be scanned could not totally fed into main memory. In this paper, an enhanced approach, called the cluster-based fuzzy-genetic mining algorithm, is thus proposed to speed up the evaluation process and keep nearly the same quality of solutions as the previous one. It divides the chromosomes in a population into k clusters by the k-means clustering approach and evaluates each individual according to both cluster and their own information. Experimental results also show the effectiveness and efficiency of the proposed approach.

Original languageEnglish
Pages (from-to)249-262
Number of pages14
JournalIEEE Transactions on Fuzzy Systems
Issue number1
StatePublished - 1 Feb 2008


  • Clustering
  • Data mining
  • Fuzzy set
  • Genetic algorithm
  • K-means

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