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JACIII Vol.17 No.2 pp. 312-317
doi: 10.20965/jaciii.2013.p0312
(2013)

Paper:

Visualization of Non-Euclidean Relational Data by Robust Linear Fuzzy Clustering Based on FCMdd Framework

Katsuhiro Honda, Takeshi Yamamoto, Akira Notsu,
and Hidetomo Ichihashi

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

Received:
June 6, 2012
Accepted:
January 13, 2013
Published:
March 20, 2013
Keywords:
relational clustering, robust clustering, linear fuzzy clustering, non-Euclidean data
Abstract

Visualization is a fundamental approach for revealing intrinsic structures in multidimensional observation. This paper considers visualization of non-Euclidean relational data by extracting local linear substructures. In order to extract robust linear clusters, an FCMdd-based linear fuzzy clustering model is applied in conjunction with a robust measure of alternative c-means. Non-Euclidean data matrices are handled with β-spread transformation in a manner similar to that of NERF c-Means. In several experiments, robust feature maps derived by the robust clustering model are compared with feature maps given by the conventional clustering model and Multi-Dimensional Scaling (MDS).

Cite this article as:
Katsuhiro Honda, Takeshi Yamamoto, Akira Notsu, and
and Hidetomo Ichihashi, “Visualization of Non-Euclidean Relational Data by Robust Linear Fuzzy Clustering Based on FCMdd Framework,” J. Adv. Comput. Intell. Intell. Inform., Vol.17, No.2, pp. 312-317, 2013.
Data files:
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