The fuzzy C-means algorithm with fuzzy P-mode prototypes for clustering objects having mixed features

Authors:
Mahnhoon Lee;Witold Pedrycz
Affiliations:
Department of Computing Science, Thompson Rivers University, 900 McGill Rd, Kamloops, BC, Canada V2C 5N3;Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada and Systems Research Institute, Polish Academy of Science, Warsaw, Poland
Venue:
Fuzzy Sets and Systems
Year:
2009

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Abstract

Frequency-based cluster prototypes have been used to cluster categorical objects, based on the simple matching dissimilarity measure. This paper introduces a new generalization called fuzzy p-mode prototype, of frequency-based prototypes. A fuzzy p-mode cluster prototype at a categorical feature is expressed as a list of p labels that have larger frequencies than others in the cluster. This paper also presents a new generalization of the fuzzy C-means clustering algorithm for the objects of mixed features. In the general fuzzy C-means clustering algorithm, any dissimilarity measures at the categorical feature level are assumed, not like other clustering algorithms that use the simple matching dissimilarity. The convergence of the general fuzzy C-means clustering algorithm under the optimization framework is proved. It is also explained through experiments over real object sets that the size of fuzzy p-mode prototypes and the fuzzification coefficients affect clustering performance.