Simultaneous Application of Fuzzy Clustering and Quantification with Incomplete Categorical Data

Katsuhiro Honda; Yoshihito Nakamura; Hidetomo Ichihashi

doi:10.20965/jaciii.2004.p0397

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JACIII Vol.8 No.4 pp. 397-402

doi: 10.20965/jaciii.2004.p0397

(2004)

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Simultaneous Application of Fuzzy Clustering and Quantification with Incomplete Categorical Data

Katsuhiro Honda, Yoshihito Nakamura, and Hidetomo Ichihashi

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

Received:

September 25, 2003

Accepted:

March 29, 2004

Published:

July 20, 2004

Keywords:

fuzzy clustering, homogeneity analysis, missing value

Abstract

This paper proposes the simultaneous application of homogeneity analysis and fuzzy clustering with incomplete data. Taking into account the similarity between the loss function for homogeneity analysis and the least squares criterion for principal component analysis, we define the new objective function in a formulation similar to linear fuzzy clustering with missing values. Numerical experiments demonstrate the feasibility of the proposed method.

Cite this article as:

K. Honda, Y. Nakamura, and H. Ichihashi, “Simultaneous Application of Fuzzy Clustering and Quantification with Incomplete Categorical Data,” J. Adv. Comput. Intell. Intell. Inform., Vol.8, No.4, pp. 397-402, 2004.

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