Robust kernel fuzzy clustering

Weiwei Du, Kohei Inoue, Kiichi Urahama

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


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 entropie 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.

Original languageEnglish
Title of host publicationFuzzy Systems and Knowledge Discovery - Second International Conference, FSKD 2005, Proceedings
PublisherSpringer Verlag
Number of pages8
Volume3613 LNAI
ISBN (Print)9783540283126
Publication statusPublished - 2006
Event2nd International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2005 - Changsa, China
Duration: Aug 27 2005Aug 29 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3613 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349


Other2nd International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2005

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Theoretical Computer Science


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