A thorough analysis of the suppressed fuzzy C-means algorithm

László Szilágyi, Sándor M. Szilágyi, Zoltán Benyó

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

3 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 added an extra computation step into the FCM iteration, which created 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 attempt to clarify the view upon the optimality and the competitive behavior of s-FCM via analytical computations and numerical analysis.

Original languageEnglish
Title of host publicationProgress in Pattern Recognition, Image Analysis and Applications - 13th Iberoamerican Congress on Pattern Recognition, CIARP 2008, Proceedings
Pages203-210
Number of pages8
DOIs
Publication statusPublished - Nov 10 2008
Event13th Iberoamerican Congress on Pattern Recognition, CIARP 2008 - Havana, Cuba
Duration: Sep 9 2008Sep 12 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5197 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other13th Iberoamerican Congress on Pattern Recognition, CIARP 2008
CountryCuba
CityHavana
Period9/9/089/12/08

    Fingerprint

Keywords

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

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Szilágyi, L., Szilágyi, S. M., & Benyó, Z. (2008). A thorough analysis of the suppressed fuzzy C-means algorithm. In Progress in Pattern Recognition, Image Analysis and Applications - 13th Iberoamerican Congress on Pattern Recognition, CIARP 2008, Proceedings (pp. 203-210). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5197 LNCS). https://doi.org/10.1007/978-3-540-85920-8_25