K-Mean Clustering and PSO: A Review
Gursharan Saini1, Harpreet Kaur2
1Gursharan Saini, M. Tech in Computer Science and Engineering from Sant Baba Bhag Singh Institute of Engineering & Technology, Padhiana, India.
2Harpreet Kaur, M. Tech at Sant Baba Bhag Singh Institute of Engineering & Technology, Padhiana, India.
Manuscript received on May 21, 2014. | Revised Manuscript received on June 17, 2014. | Manuscript published on June 30, 2014. | PP: 112-114 | Volume-3, Issue-5, June 2014. | Retrieval Number: E3139063514/2013©BEIESP
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© The Authors. 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: Clustering is a method which divides data objects into groups based on the information found in data that describes the objects and relationships among them. There are a variety of algorithms have been developed in recent years for solving problems of data clustering. Data clustering algorithms can be either hierarchical or partitioned. Most promising among them are K-means algorithm which is partitioned clustering algorithm .Moreover k-mean Algorithm is an efficient Clustering Algorithm but it can generate a local optimal solution. On the other hand, Particle Swarm Optimization is used for global optimization. Thus K-means algorithm shows improved results when used with the combination of PSO (Particle Swarm Optimization).
Keywords: Data clustering, Data Mining, K-Mean PSO.