An Efficient Hybrid Particle Swarm Optimization for Data Clustering

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 i...

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Bibliographic Details
Published in:International Journal of Data Mining & Knowledge Management Process 2012-11, Vol.2 (6), p.13-25
Main Author: Seetha, M
Format: Article
Language:English
Subjects:
Algorithms
FPSO
Fuzzy
Fuzzy logic
Fuzzy set theory
Mathematical analysis
Mathematical models
Optimization
Online Access:Get full text
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Summary: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 near-optimal 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).
ISSN:2231-007X
2230-9608
DOI:10.5121/ijdkp.2012.2602