Modified particle swarm optimization for pattern clustering

Swetha, KP and Devi, Susheela V (2012) Modified particle swarm optimization for pattern clustering. In: Proceedings of the 19th International Conference, ICONIP 2012, November 12-15, 2012, Doha, Qatar.

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Abstract

Data clustering is a common technique for statistical data analysis, which is used in many fields, including machine learning and data mining. Clustering is grouping of a data set or more precisely, the partitioning of a data set into subsets (clusters), so that the data in each subset (ideally) share some common trait according to some defined distance measure. In this paper we present the genetically improved version of particle swarm optimization algorithm which is a population based heuristic search technique derived from the analysis of the particle swarm intelligence and the concepts of genetic algorithms (GA). The algorithm combines the concepts of PSO such as velocity and position update rules together with the concepts of GA such as selection, crossover and mutation. The performance of the above proposed algorithm is evaluated using some benchmark datasets from Machine Learning Repository. The performance of our method is better than k-means and PSO algorithm.

Item Type:	Conference Paper
Publisher:	Springer
Additional Information:	Copyright of this article belongs to Springer.
Keywords:	Data Clustering; Particle Swarm Optimization; Fitness Function; Genetic Algorithms
Department/Centre:	Division of Electrical Sciences > Computer Science & Automation
Date Deposited:	29 Nov 2013 05:41
Last Modified:	04 Dec 2013 12:29
URI:	http://eprints.iisc.ac.in/id/eprint/47830

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