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Erschienen in:
2005 | OriginalPaper | Buchkapitel
Robust Kernel Fuzzy Clustering
verfasst von : Weiwei Du, Kohei Inoue, Kiichi Urahama
Erschienen in: Fuzzy Systems and Knowledge Discovery
Verlag: Springer Berlin Heidelberg
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We present a method for extracting arbitrarily shaped clusters buried in uniform noise data. The popular
k
-means algorithm is firstly fuzzified with addition of entropic terms to the objective function of data partitioning problem. This fuzzy clustering is then kernelized for adapting to the arbitrary shape of clusters. Finally, the Euclidean distance in this kernelized fuzzy clustering is modified to a robust one for avoiding the influence of noisy background data. This robust kernel fuzzy clustering method is shown to outperform every its predecessor: fuzzified
k
-means, robust fuzzified
k
-means and kernel fuzzified
k
-means algorithms.