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Simultaneous feature selection and clustering using particle swarm optimization

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

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Official URL: http://dx.doi.org/10.1007/978-3-642-34475-6_61

Abstract

Data clustering groups data so that data which are similar to each other are in the same group and data which are dissimilar to each other are in different groups. Since generally clustering is a subjective activity, it is possible to get different clusterings of the same data depending on the need. This paper attempts to find the best clustering of the data by first carrying out feature selection and using only the selected features, for clustering. A PSO (Particle Swarm Optimization)has been used for clustering but feature selection has also been carried out simultaneously. The performance of the above proposed algorithm is evaluated on some benchmark data sets. The experimental results shows the proposed methodology outperforms the previous approaches such as basic PSO and Kmeans for the clustering problem.

Item Type: Conference Paper
Additional Information: Copyright of this article belongs to Springer.
Keywords: Data Clustering; Particle Swarm Optimization; Feature Selection; Fitness Function
Department/Centre: Division of Electrical Sciences > Computer Science & Automation
Depositing User: Francis Jayakanth
Date Deposited: 29 Nov 2013 05:45
Last Modified: 04 Dec 2013 12:31
URI: http://eprints.iisc.ac.in/id/eprint/47831

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