Analytical and numerical evaluation of the suppressed fuzzy c-means algorithm

László Szilágyi, Sándor M. Szilágyi, Zoltán Benyó

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

6 Citations (Scopus)

Abstract

Suppressed fuzzy c-means (s-FCM) clustering was introduced in [Fan, J. L., Zhen, W. Z., Xie, W. X.: Suppressed fuzzy c-means clustering algorithm. Patt. Recogn. Lett. 24, 1607-1612 (2003)] with the intention of combining the higher speed of hard c-means (HCM) clustering with the better classification properties of fuzzy c-means (FCM) algorithm. They modified the FCM iteration to create a competition among clusters: lower degrees of memberships were diminished according to a previously set suppression rate, while the largest fuzzy membership grew by swallowing all the suppressed parts of the small ones. Suppressing the FCM algorithm was found successful in the terms of accuracy and working time, but the authors failed to answer a series of important questions. In this paper we clarify the view upon the optimality and the competitive behavior of s-FCM via analytical computations and numerical analysis.

Original languageEnglish
Title of host publicationModeling Decisions for Artificial Intelligence - 5th International Conference, MDAI 2008, Proceedings
PublisherSpringer Verlag
Pages146-157
Number of pages12
ISBN (Print)3540882685, 9783540882688
DOIs
Publication statusPublished - Jan 1 2008
Event5th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2008 - Sabadell, Spain
Duration: Oct 30 2008Oct 31 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5285 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other5th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2008
CountrySpain
CitySabadell
Period10/30/0810/31/08

Keywords

  • Alternating optimization
  • Competitive clustering
  • Fuzzy c-means algorithm
  • Suppressed fuzzy c-means algorithm

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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    Szilágyi, L., Szilágyi, S. M., & Benyó, Z. (2008). Analytical and numerical evaluation of the suppressed fuzzy c-means algorithm. In Modeling Decisions for Artificial Intelligence - 5th International Conference, MDAI 2008, Proceedings (pp. 146-157). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5285 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-540-88269-5-14