Enhanced the Performance of Self-Optimal Clustering Technique Using Particle Swarm Optimization
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2017 by IJETT Journal|
|Year of Publication : 2017|
|Authors : Kavita Firke, Dinesh Kumar Sahu
|DOI : 10.14445/22315381/IJETT-V44P212|
Kavita Firke, Dinesh Kumar Sahu"Enhanced the Performance of Self-Optimal Clustering Technique Using Particle Swarm Optimization", International Journal of Engineering Trends and Technology (IJETT), V44(2),58-62 February 2017. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
Self-optimal clustering technique is great advantage over partition clustering technique. the partition clustering technique faced a problem of the generation of cluster and quality validation of generated cluster. The optimal clustering technique automatically decided the number of cluster according to their selection of center point and generation of cluster. In this paper used particle swarm optimization technique for the fitness constraints function for the selection of center point. the particle of swarm optimization gives the dual fitness constraints for the selection of cluster center and quality validation. The modified self-optimal clustering technique implemented in MATLAB software and used reputed data set from UCI. Our experimental result shows that better performance instead of SOC algorithm.
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PSO, SOC, Clustering, EA, SVM.