FCM-Type Fuzzy Clustering of Mixed Databases Considering Nominal Variable Quantification

Abstract

This paper proposes a clustering algorithm that performs FCM-type clustering of datasets including categorical data. The proposed algorithm iterates categorical data quantification in FCE clustering so that quantified scores suit the current fuzzy partition. The objective function is the linear combination of two cost functions, i.e., the objective function of FCE clustering and the clustering criterion of quantified category scores. Because quantified category scores are assigned considering the relationship among categories, they are useful for interpreting the cluster structure.

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FCM-Type Fuzzy Clustering of Mixed Databases Considering Nominal Variable Quantification

Katsuhiro Honda, Ryo Uesugi, and Hidetomo Ichihashi

Graduate School of Engineering, Osaka Prefecture University, 1-1 Gakuen-cho, Nakaku, Sakai, Osaka 599-8531, Japan

Received: October 28, 2005

Accepted: December 8, 2006

Keywords: fuzzy clustering, quantification of categorical data, mixed database

Abstract

Reference