Finding fuzzy classification rules using data mining techniques

  • Authors:

  • Yi-Chung Hu;Ruey-Shun Chen;Gwo-Hshiung Tzeng

  • Affiliations:

  • Institute of Information Management, National Chiao Tung University, Hsinchu 300, Taiwan, ROC;Institute of Information Management, National Chiao Tung University, Hsinchu 300, Taiwan, ROC;Institute of Management of Technology, National Chiao Tung University, Hsinchu 300, Taiwan, ROC

  • Venue:
  • Pattern Recognition Letters
  • Year:
  • 2003

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Abstract

Data mining techniques can be used to discover useful patterns by exploring and analyzing data, so, it is feasible to incorporate data mining techniques into the classification process to discover useful patterns or classification rules from training samples. This paper thus proposes a data mining technique to discover fuzzy classification rules based on the well-known Apriori algorithm. Significantly, since it is difficult for users to specify the minimum fuzzy support used to determine the frequent fuzzy grids or the minimum fuzzy confidence used to determine the effective classification rules derived from frequent fuzzy grids, therefore the genetic algorithms are incorporated into the proposed method to determine those two thresholds with binary chromosomes. For classification generalization ability, the simulation results from the iris data and the appendicitis data demonstrate that the proposed method performs well in comparison with other classification methods.