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JACIII Vol.11 No.2 pp. 162-167
doi: 10.20965/jaciii.2007.p0162
(2007)

Paper:

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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
Published:
February 20, 2007
Creative Commons License
Keywords:
fuzzy clustering, quantification of categorical data, mixed database
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.
Cite this article as:
K. Honda, R. Uesugi, and H. Ichihashi, “FCM-Type Fuzzy Clustering of Mixed Databases Considering Nominal Variable Quantification,” J. Adv. Comput. Intell. Intell. Inform., Vol.11 No.2, pp. 162-167, 2007.
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