Journal of Japan Society for Fuzzy Theory and Intelligent Informatics
Online ISSN : 1881-7203
Print ISSN : 1347-7986
ISSN-L : 1347-7986
Original Papers
Local Principal Component Analysis for Mixed Databases Based on Linear Fuzzy Clustering
Ryo UESUGIKatsuhiro HONDAHidetomo ICHIHASHIAkira NOTSU
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2007 Volume 19 Issue 3 Pages 287-298

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Abstract
Fuzzy c-Varieties(FCV) is a tool for linear fuzzy clustering and is also applicable to local principal component analysis (local PCA), in which each low-dimensional subspace is estimated considering data partition. Although the clustering criterion in FCV is distances between data points and prototypical linear varieties, the criterion can also be defined based on least square approximation. Optimal scaling is a useful approach to multivariate analysis for mixed databases and has been applied to linear model estimation. This paper proposes two formulations of local PCA for mixed databases based on optimal scaling, in which a conventional FCV and linear fuzzy clustering using least square approximation are enhanced. The proposed algorithms include a step of calculating numerical scores of categorical variables in addition to the ordinary alternative optimization.
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© 2007 Japan Society for Fuzzy Theory and Intelligent Informatics
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