Title :
Fuzzy Clustering with Improved Artificial Fish Swarm Algorithm
Author :
He, Si ; Belacel, Nabil ; Hamam, Habib ; Bouslimani, Yassine
Author_Institution :
Electr. Eng. Dept., Univ. de Moncton, Moncton, NB, Canada
Abstract :
This 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.
Keywords :
fuzzy set theory; genetic algorithms; pattern clustering; ATSA; FCM; GA; artificial fish swarm algorithm; fuzzy C-means algorithm; fuzzy clustering; genetic algorithm; Artificial intelligence; Clustering algorithms; Clustering methods; Councils; Genetic algorithms; Helium; Information technology; Marine animals; Particle swarm optimization; Pattern recognition; Artificial Fish Swarm Algorithm; Fuzzy Clustering; fuzzy C-Means;
Conference_Titel :
Computational Sciences and Optimization, 2009. CSO 2009. International Joint Conference on
Conference_Location :
Sanya, Hainan
Print_ISBN :
978-0-7695-3605-7
DOI :
10.1109/CSO.2009.367
