A Survey on K-mean Clustering and Particle Swarm Optimization
Pritesh Vora1, Bhavesh Oza 2

1Pritesh Vora, Information Technology, Gujarat Technological University/ L.D. College of Engineering, Ahmedabad, India.
2Prof. Bhavesh Oza, Computer Engineering Department Gujarat Technological University/ L.D. College of Engineering, Ahmedabad, India.
Manuscript received on February 05, 2013. | Revised Manuscript received on February 12, 2013. | Manuscript published on February 15, 2013. | PP: 24-26 | Volume-1 Issue-3, February 2013. | Retrieval Number: C0150020213/2013©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: In Data Mining, Clustering is an important research topic and wide range of unsupervised classification application. Clustering is technique which divides a data into meaningful groups. K-mean is one of the popular clustering algorithms. K-mean clustering is widely used to minimize squared distance between features values of two points reside in the same cluster. Particle swarm optimization is an evolutionary computation technique which finds optimum solution in many applications. Using the PSO optimized clustering results in the components, in order to get a more precise clustering efficiency. In this paper, we present the comparison of K-mean clustering and the Particle swarm optimization.
Keywords: Clustering, K-mean Clustering, Particle Swarm Optimization.