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A Stochastic Connectionist Approach for Global Optimization with Application to Pattern Clustering

Babu, Phanendra G and Murty, Narasimha M and Keerthi, Sathiya S (2000) A Stochastic Connectionist Approach for Global Optimization with Application to Pattern Clustering. In: IEEE Transactions on Systems Man and Cybernetics Part B Cybernetics, 30 (1). pp. 10-24.

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Abstract

In this paper, a stochastic connectionist approach is proposed for solving function optimization problems with real-valued parameters. With the assumption of increased processing capability of a node in the connectionist network, we show how a broader class of problems can be solved. As the proposed approach is a stochastic search technique, it avoids getting stuck in local optima. Robustness of the approach is demonstrated on several multi-modal functions with different numbers of variables. Optimization of a well-known partitional clustering criterion, the squared-error criterion (SEC), is formulated as a function optimization problem and is solved using the proposed approach. This approach is used to cluster selected data sets and the results obtained are compared with that of the K-means algorithm and a simulated annealing (SA) approach. The amenability of the connectionist approach to parallelization enables effective use of parallel hardware

Item Type: Journal Article
Additional Information: ©2000 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Keywords: Clustering;connectionist approaches;function;optimization;global optimization
Department/Centre: Division of Electrical Sciences > Computer Science & Automation (Formerly, School of Automation)
Date Deposited: 25 Aug 2008
Last Modified: 19 Sep 2010 04:15
URI: http://eprints.iisc.ernet.in/id/eprint/1733

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