International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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Volume 119 - Issue 20 |
Published: June 2015 |
Authors: G. Malini Devi, M.Seetha, K.V.N.Sunitha |
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G. Malini Devi, M.Seetha, K.V.N.Sunitha . A Novel Hybrid Clustering Techniques based on K-Means, PSO and Dynamic Optimization. International Journal of Computer Applications. 119, 20 (June 2015), 20-25. DOI=10.5120/21184-4258
@article{ 10.5120/21184-4258, author = { G. Malini Devi,M.Seetha,K.V.N.Sunitha }, title = { 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 }, doi = { 10.5120/21184-4258 }, publisher = { Foundation of Computer Science (FCS), NY, USA } }
%0 Journal Article %D 2015 %A G. Malini Devi %A M.Seetha %A K.V.N.Sunitha %T A Novel Hybrid Clustering Techniques based on K-Means, PSO and Dynamic Optimization%T %J International Journal of Computer Applications %V 119 %N 20 %P 20-25 %R 10.5120/21184-4258 %I Foundation of Computer Science (FCS), NY, USA
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.