Realization of feature description systems for clusters by rule generation based on genetic programming and its applications

SUMMARY This paper deals with the realization of feature description systems for clusters by rule generation based on genetic programming (GP) and its applications. First, the data are divided into several clusters by using conventional clustering algorithms. Then logical variables corresponding to the categorical variables are introduced, and the logical expressions using these logical variables are defined as rules to extract the targeted cluster from the dataset. The rules are improved by GP so that they are valid (become true) only for the targeted cluster. Unlike ordinary GP procedures, the fitness of individuals is defined as proportional to the number of hits inside the targeted cluster, but also to the inverse of the number of hits outside the targeted cluster. In simulation studies, the system is applied first to artificially generated samples and clusters to examine the performance of the system, and then to personal loan assessment problems, after which the evaluation of several kinds of clustering problems is summarized. © 2007 Wiley Periodicals, Inc. Electron Comm Jpn Pt 2, 90(9): 87–97, 2007; Published online in Wiley InterScience (www. interscience.wiley.com). DOI 10.1002/ecjb.20380

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