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Multivariate fuzzy k-modes algorithm
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DOI：10.1007/s10044-015-0465-3.png)
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Abstract
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In the fuzzy k-modes clustering, there is just one membership degree of interest by class for each individual which cannot be sufficient to model ambiguity of data precisely. It is known that the essence of a multivariate thinking allows to expose the inherent structure and meaning revealed within a set of variables classified. In this paper, a multivariate approach for membership degrees is presented to better handle ambiguous data that share properties of different clusters. This method is compared with other fuzzy k-modes methods of the literature based on a multivariate internal index that is also proposed in this paper. Synthetic and real categorical data sets are considered in this study.
Keywords:
Fuzzy clustering
Unsupervised pattern recognition
Multivariate membership degrees
Categorical data
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