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A new approach to robust k-Means clustering based on fuzzy principal component analysis

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4 Author(s)
Katsuhiro Honda ; Department of Computer Science and Intelligent Systems, Osaka Prefecture University, 1-1 Gakuen-cho, Nakaku, Sakai, 599-8531 Japan ; Hiromichi Araki ; Tomohiro Matsui ; Hidetomo Ichihashi

PCA-guided k-Means performs non-hierarchical hard clustering based on PCA-guided subspace learning mechanism in a batch process. Sequential Fuzzy Cluster Extraction (SFCE) is a procedure for analytically extracting fuzzy clusters one by one, and is useful for ignoring noise samples. This paper considers a hybrid concept of the two clustering approaches and proposes a new robust k-Means algorithm that is based on a fuzzy PCA-guided clustering procedure. In the proposed method, a responsibility weight of each sample in k-Means process is estimated based on the noise fuzzy clustering mechanism, and cluster membership indicators in k-Means process are derived as fuzzy principal components considering the responsibility weights in fuzzy PCA.

Published in:

2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)

Date of Conference:

1-8 June 2008