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A Novel Hybrid Clustering Techniques based on K-Means, PSO and Dynamic Optimization

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International Journal of Computer Applications
© 2015 by IJCA Journal
Volume 119 - Number 20
Year of Publication: 2015
Authors:
G. Malini Devi
M. Seetha
K. V. N. Sunitha
10.5120/21184-4258

Malini G Devi, M.seetha and K.v.n.sunitha. Article: A Novel Hybrid Clustering Techniques based on K-Means, PSO and Dynamic Optimization. International Journal of Computer Applications 119(20):20-25, June 2015. Full text available. BibTeX

@article{key:article,
	author = {G. Malini Devi and M.seetha and K.v.n.sunitha},
	title = {Article: A Novel Hybrid Clustering Techniques based on K-Means, PSO and Dynamic Optimization},
	journal = {International Journal of Computer Applications},
	year = {2015},
	volume = {119},
	number = {20},
	pages = {20-25},
	month = {June},
	note = {Full text available}
}

Abstract

Clustering is a process for partitioning datasets. This technique is a challenging field of research in which their potential applications pose their own special requirements. K-Means is the most extensively used algorithm to find a partition that minimizes Mean Square Error (MSE) is an exigent task. The Object Function of the K-Means is not convex and hence it may contain local minima. ACO methods are useful in problems that need to find paths to goals. Particle swarm optimization (PSO) is a global optimization algorithm for dealing with problems in which a best solution can be represented as a point or surface in an n-dimensional space. But PSO algorithm suffers from slow convergence near optimal solution. In this paper a new modified sequential clustering approach is proposed, which uses PSO in combination with K-Means & dynamic optimization algorithm for data clustering. This approach overcomes drawbacks of K-means, PSO technique, improves clustering and avoids being trapped in a local optimal solution. It was ascertained that the K-Means, PSO, KPSOK & dynamic optimization algorithms are proposed among these algorithms dynamic optimization results in accurate, robust and better clustering.

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