Quantification of multivariate categorical data considering clusters of items and individuals

Authors:
Chi-Hyon Oh;Katsuhiro Honda;Hidetomo Ichihashi
Affiliations:
Faculty of Liberal Arts and Sciences, Osaka University of Economics and Law, Osaka, Japan;Graduate School of Engineering, Osaka Prefecture University, Osaka, Japan;Graduate School of Engineering, Osaka Prefecture University, Osaka, Japan
Venue:
MDAI'05 Proceedings of the Second international conference on Modeling Decisions for Artificial Intelligence
Year:
2005

Citing 2
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Linear fuzzy clustering techniques with missing values and their application to local principal component analysis

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Abstract

This paper proposes a simultaneous application of homogeneity analysis and fuzzy clustering which simultaneously partitions individuals and items in categorical multivariate data sets. Taking the similarity between the loss of homogeneity in homogeneity analysis and the least squares criterion in principal component analysis into account, the new objective function is defined in a similar formulation to the linear fuzzy clustering.