Evolutionary Fuzzy Rule Induction Process for Subgroup Discovery: A Case Study in Marketing

Authors:
MJ. del Jesus;P. Gonzalez;F. Herrera;M. Mesonero
Affiliations:
Jaen Univ., Jaen;-;-;-
Venue:
IEEE Transactions on Fuzzy Systems
Year:
2007

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Abstract

This paper presents a genetic fuzzy system for the data mining task of subgroup discovery, the subgroup discovery iterative genetic algorithm (SDIGA), which obtains fuzzy rules for subgroup discovery in disjunctive normal form. This kind of fuzzy rule allows us to represent knowledge about patterns of interest in an explanatory and understandable form that can be used by the expert. Experimental evaluation of the algorithm and a comparison with other subgroup discovery algorithms show the validity of the proposal. SDIGA is applied to a market problem studied in the University of Mondragon, Spain, in which it is necessary to extract automatically relevant and interesting information that helps to improve fair planning policies. The application of SDIGA to this problem allows us to obtain novel and valuable knowledge for experts.