Paper Info
Title
Novel Clustering Algorithms Based on Improved Artificial Fish Swarm Algorithm
Abstract
An improved artificial fish swarm algorithm (IAFSA) is proposed, and its complexity is much less than the original algorithm (AFSA) because of a new proposed fish behavior. Based on IAFSA, two novel algorithms for data clustering are presented. One is the improved artificial fish swarm clustering (IAFSC) algorithm, the other is a hybrid fuzzy clustering algorithm that incorporates the fuzzy c-means (FCM) into the IAFSA. The performance of the proposed algorithms is compared with that of the particle swarm optimization (PSO), k-means and FCM respectively on Iris testing data. Simulation results show that the performance of the proposed algorithms is much better than that of the PSO, K-means and FCM. And the proposed hybrid fuzzy clustering algorithm avoids the FCM's weakness such as initialization value problem and local minimum problem.
Year
DOI
Venue
2009
10.1109/FSKD.2009.534
FSKD (3)
Keywords
Field
DocType
novel algorithm,iris testing data,artificial life,fuzzy set theory,pattern clustering,improved artificial fish swarm clustering,improved artificial fish swarm,fuzzy c-means,improved artificial fish swarm algorithm,particle swarm optimisation,local minimum problem,k-means,fish behavior,novel clustering,hybrid fuzzy clustering algorithm,initialization value problem,proposed algorithm,improved artificial fish,clustering algorithms,data clustering,artificial fish swarm algorithm,new proposed fish behavior,original algorithm,swarm algorithm,particle swarm optimization,fuzzy clustering,iris,optimization,k means,initial value problem,mathematical model,data mining
Fuzzy clustering,Swarm behaviour,Computer science,Fuzzy set,Artificial intelligence,Cluster analysis,Particle swarm optimization,k-means clustering,Canopy clustering algorithm,Pattern recognition,Fuzzy logic,Algorithm,Machine learning
Conference
Volume
ISBN
Citations 
3
978-0-7695-3735-1
7
PageRank 
References 
Authors
0.94
8
3
Name
Order
Citations
PageRank
Yongming Cheng1172.19
Mingyan Jiang26711.96
dongfeng yuan318043.88