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JACIII Vol.15 No.1 pp. 83-89
doi: 10.20965/jaciii.2011.p0083
(2011)

Paper:

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Variable Weighting in PCA-Guided k-Means and its Connection with Information Summarization

Katsuhiro Honda, Akira Notsu, and Hidetomo Ichihashi

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

Received:
February 9, 2010
Accepted:
April 22, 2010
Published:
January 20, 2011
Creative Commons License
Keywords:
k-means clustering, principal component analysis, variable weighting
Abstract
In the present paper, a variable selection model in k-Means is proposed, in which a variable weighting mechanism is introduced to PCA-guided k-Means. Variable weights are estimated in a manner similar to FCM clustering, while the membership indicator is derived using a PCA-guided method, in which the principal component scores are calculated by considering the variable weights. The variable weights emphasize the variables that have meaningful cluster information in the calculation of the membership indicators, and the absolute responsibility of each variable is revealed by soft transition to possibilistic values. It is also shown that the variable weights are derived in a manner similar to variable selection for PCA, with the goal being information summarization. The characteristics of the proposed method are demonstrated in an application to document clustering.
Cite this article as:
K. Honda, A. Notsu, and H. Ichihashi, “Variable Weighting in PCA-Guided k-Means and its Connection with Information Summarization,” J. Adv. Comput. Intell. Intell. Inform., Vol.15 No.1, pp. 83-89, 2011.
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