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
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;
Conference_Titel :
Evolutionary Computation, 2003. CEC '03. The 2003 Congress on
Print_ISBN :
0-7803-7804-0
DOI :
10.1109/CEC.2003.1299577
