Title :
Multiple Cooperating Swarms for Data Clustering
Author :
Ahmadi, Abbas ; Karray, Fakhri ; Kamel, Mohamed
Author_Institution :
Dept. of Syst. Design Eng., Waterloo Univ., Ont.
Abstract :
A new clustering technique by the use of multiple swarms is proposed. The proposed technique mimics the behavior of biological swarms which explore food situated in several places. We model the clustering problem using particle swarm optimization (PSO) approach. The proposed method considers multiple cooperating swarms to find centers of clusters. By assigning a portion of the solution space to each swarm, the exploration ability to find the solution is enhanced. Moreover, the cooperation among swarms increases the between-class distance. The proposed method outperforms k-means clustering as well as conventional PSO-based clustering techniques
Keywords :
particle swarm optimisation; pattern clustering; data clustering; multiple cooperating swarms; particle swarm optimization; Biology computing; Birds; Data engineering; Design engineering; Educational institutions; Equations; Marine animals; Particle swarm optimization; Pattern analysis; Space exploration; Multiple swarms; clustering; particle swarm optimization(PSO);
Conference_Titel :
Swarm Intelligence Symposium, 2007. SIS 2007. IEEE
Conference_Location :
Honolulu, HI
Print_ISBN :
1-4244-0708-7
DOI :
10.1109/SIS.2007.368047