Fuzzy Clustering with Improved Artificial Fish Swarm

DOIResolve DOI: http://doi.org/10.1109/CSO.2009.367
AuthorSearch for: ; Search for: ; Search for: ; Search for:
TypeArticle
Proceedings titleProceedings of the Second International Joint Conference on Computational Sciences and Optimization (CSO 2009)
Conference2009 International Joint Conference on Computational Sciences and Optimization, CSO 2009, Sanya, Hainan, China, 24-26 April 2009
ISBN978-0-7695-3605-7
Volume2
Pages317321; # of pages: 5
Subjectfuzzy set theory; genetic algorithms; pattern clustering
AbstractThis paper applies the artificial fish swarm algorithm (AFSA) to fuzzy clustering. An improved AFSA with adaptive visual and adaptive step is proposed. AFSA enhances the performance of the fuzzy C-means (FCM) algorithm. A computational experiment shows that AFSA improved FCM out performs both the conventional FCM algorithm and the genetic algorithm (GA) improved FCM.
Publication date
PublisherIEEE Computer Society
LanguageEnglish
AffiliationNational Research Council Canada (NRC-CNRC); NRC Institute for Information Technology
Peer reviewedYes
NRC number50760
NPARC number15261133
Export citationExport as RIS
Report a correctionReport a correction
Record identifier2281de57-2ce5-451a-ab98-89434323e7e5
Record created2010-06-10
Record modified2016-05-09
Bookmark and share
  • Share this page with Facebook (Opens in a new window)
  • Share this page with Twitter (Opens in a new window)
  • Share this page with Google+ (Opens in a new window)
  • Share this page with Delicious (Opens in a new window)