Paper Info
Title
Fuzzy Clustering with Improved Artificial Fish Swarm Algorithm
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.
Year
DOI
Venue
2009
10.1109/CSO.2009.367
CSO (2)
Keywords
Field
DocType
fuzzy clustering,fuzzy set theory,conventional fcm algorithm,pattern clustering,fuzzy c-means,artificial fish swarm algorithm,improved fcm,improved afsa,fcm,adaptive step,computational experiment,genetic algorithm,improved artificial fish,genetic algorithms,fuzzy c-means algorithm,atsa,ga,adaptive visual,swarm algorithm,pattern recognition,computer experiment,classification algorithms,artificial intelligence,convergence,helium,particle swarm optimization,optimization,data mining,visualization,information technology,clustering algorithms
Convergence (routing),Fuzzy clustering,Swarm behaviour,Computer science,Fuzzy set,Artificial intelligence,Cluster analysis,Genetic algorithm,Pattern recognition,Fuzzy logic,Algorithm,Statistical classification,Machine learning
Conference
Volume
ISBN
Citations 
2
978-0-7695-3605-7
16
PageRank 
References 
Authors
1.11
5
4
Name
Order
Citations
PageRank
Si He1161.78
Nabil Belacel230127.07
habib hamam312423.13
Yassine Bouslimani4172.50