TITLE

AN EFFICIENT HYBRID PARTICLE SWARM OPTIMIZATION FOR DATA CLUSTERING

AUTHOR(S)
Seetha, M.; Devi, G. Malini; Sunitha, K. V. N.
PUB. DATE
November 2012
SOURCE
International Journal of Data Mining & Knowledge Management Proc;Nov2012, Vol. 2 Issue 6, p13
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
This paper presents an efficient hybrid method, namely fuzzy particle swarm optimization (FPSO) and fuzzy c-means (FCM) algorithms, to solve the fuzzy clustering problem, especially for large sizes. When the problem becomes large, the FCM algorithm may result in uneven distribution of data, making it difficult to find an optimal solution in reasonable amount of time. The PSO algorithm does find a good or nearoptimal solution in reasonable time, but its performance was improved by seeding the initial swarm with the result of the c-means algorithm. The fuzzy c-means, PSO and FPSO algorithms are compared using the performance factors of object function value (OFV) and CPU execution time. It was ascertained that the computational times for the FPSO method outperforms the FCM and PSO method and had higher solution quality in terms of the objective function value (OFV).
ACCESSION #
84322557

 

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