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- W1975879820 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." @default.
- W1975879820 created "2016-06-24" @default.
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- W1975879820 date "2009-12-01" @default.
- W1975879820 modified "2023-10-10" @default.
- W1975879820 title "The fuzzy C-means algorithm with fuzzy P-mode prototypes for clustering objects having mixed features" @default.
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- W1975879820 doi "https://doi.org/10.1016/j.fss.2009.06.015" @default.
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