Robust kernel fuzzy clustering

Weiwei Du, Kohei Inoue, Kiichi Urahama

研究成果: Contribution to journalArticle査読

11 被引用数 (Scopus)

抄録

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.

本文言語英語
ページ(範囲)454-461
ページ数8
ジャーナルUnknown Journal
3613
PART I
出版ステータス出版済み - 2005

All Science Journal Classification (ASJC) codes

  • ハードウェアとアーキテクチャ

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