Fuzzy c-varieties/elliptotypes clustering in reproducing kernel Hilbert space
作者： Jacek M. Łęski
DOI: 10.1016/S0165-0114(03)00184-2
关键词:
摘要: Abstract Fuzzy clustering algorithms are successfully applied to a wide variety of problems, such as: pattern recognition, image analysis, modeling and so on. The C-Means (FCM) method is one the most popular methods based on minimization criterion function. However, performance FCM good only when data set contains clusters that approximately same size shape. In this paper, simple idea will be used, overcome problem. original input (data) space mapped into high (possibly infinite)-dimensional feature F through some nonlinear mapping. structures modeled by linear varieties or elliptotypes. This called Kernel C-Varieties/Elliptotypes algorithm. Performance new algorithm experimentally compared with fuzzy c-varieties/elliptotypes using synthetic datasets real-life datasets.
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