Generalized Fuzzy c-Means Clustering and its Property of Fuzzy Classification Function
Yuchi Kanzawa* and Sadaaki Miyamoto**
*Shibaura Institute of Technology
3-7-5 Toyosu, Koto, Tokyo 135-8548, Japan
**University of Tsukuba
1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan
This study shows that a generalized fuzzy c-means (gFCM) clustering algorithm, which covers both standard and exponential fuzzy c-means clustering, can be constructed if a given fuzzified function, its derivative, and its inverse derivative can be calculated. Furthermore, our results show that the fuzzy classification function for gFCM exhibits a behavior similar to that of both standard and exponential fuzzy c-means clustering.
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