DocumentCode
2465322
Title
A Novel Fuzzy Clustering Based on Particle Swarm Optimization
Author
Li, Lili ; Liu, Xiyu ; Xu, Mingming
Author_Institution
Shandong Normal Univ., Jinan
fYear
2007
fDate
23-25 Nov. 2007
Firstpage
88
Lastpage
90
Abstract
In order to overcome the shortcomings of fuzzy C-means algorithm such as the local optima and sensitivity to initialization, a new PSO-based fuzzy algorithm is discussed in this paper. The new algorithm uses the capacity of global search in PSO algorithm, and solves the problems of FCM. The experiment shows that the algorithm avoids the local optima and increases the convergence speed.
Keywords
fuzzy set theory; particle swarm optimisation; pattern clustering; search problems; PSO-based fuzzy algorithm; fuzzy C-means algorithm; global search; novel fuzzy clustering; particle swarm optimization; Clustering algorithms; Convergence; Engineering management; Evolutionary computation; Information science; Iterative algorithms; Particle swarm optimization; Partitioning algorithms; Stochastic processes; Unsupervised learning; Cluster analysis; Fuzzy C-means algorithm; Global optimization; Particle swarm optimization;
fLanguage
English
Publisher
ieee
Conference_Titel
Information Technologies and Applications in Education, 2007. ISITAE '07. First IEEE International Symposium on
Conference_Location
Kunming
Print_ISBN
978-1-4244-1386-7
Electronic_ISBN
978-1-4244-1386-7
Type
conf
DOI
10.1109/ISITAE.2007.4409243
Filename
4409243
Link To Document