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

• Published in: International journal of computer applications 2015-01, Vol.119 (20), p.20-25
• Main Authors: Devi, G Malini, Seetha, M, N Sunitha, K V
• Format: Article
• Language: English
• Subjects: Algorithms; Clustering; Convergence; Dynamics; Mathematical models; Optimization; Swarm intelligence; Tasks
• ISSN: 0975-8887; 0975-8887
• DOI: 10.5120/21184-4258

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.