DocumentCode
2998307
Title
Data clustering using particle swarm optimization
Author
van der Merwe, D.W. ; Engelbrecht, Andries P.
Author_Institution
Dept. of Comput. Sci., Pretoria Univ., South Africa
Volume
1
fYear
2003
fDate
8-12 Dec. 2003
Firstpage
215
Abstract
This paper proposes two new approaches to using PSO to cluster data. It is shown how PSO can be used to find the centroids of a user specified number of clusters. The algorithm is then extended to use K-means clustering to seed the initial swarm. This second algorithm basically uses PSO to refine the clusters formed by K-means. The new PSO algorithms are evaluated on six data sets, and compared to the performance of K-means clustering. Results show that both PSO clustering techniques have much potential.
Keywords
evolutionary computation; optimisation; pattern clustering; K-means clustering; PSO algorithms; PSO clustering techniques; cluster centroid; cluster data; data clustering; particle swarm optimization; Clustering algorithms; Computer science; Data analysis; Data mining; Image segmentation; Machine learning; Machine learning algorithms; Particle swarm optimization; Scalability; Topology;
fLanguage
English
Publisher
ieee
Conference_Titel
Evolutionary Computation, 2003. CEC '03. The 2003 Congress on
Print_ISBN
0-7803-7804-0
Type
conf
DOI
10.1109/CEC.2003.1299577
Filename
1299577
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