Generalized weighted conditional fuzzy clustering

Authors:
J. M. Leski
Affiliations:
Div. of Biomed. Electron., Silesian Univ. of Technol., Gliwice, Poland
Venue:
IEEE Transactions on Fuzzy Systems
Year:
2003

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Abstract

Fuzzy clustering helps to find natural vague boundaries in data. The fuzzy c-means method is one of the most popular clustering methods based on minimization of a criterion function. Among many existing modifications of this method, conditional or context-dependent c-means is the most interesting one. In this method, data vectors are clustered under conditions based on linguistic terms represented by fuzzy sets. This paper introduces a family of generalized weighted conditional fuzzy c-means clustering algorithms. This family include both the well-known fuzzy c-means method and the conditional fuzzy c-means method. Performance of the new clustering algorithm is experimentally compared with fuzzy c-means using synthetic data with outliers and the Box-Jenkins database.