Please use this identifier to cite or link to this item: https://doi.org/10.1109/3477.826943
Title: A stochastic connectionist approach for global optimization with application to pattern clustering
Authors: Babu, G.P.
Murty, M.N.
Keerthi, S.S. 
Keywords: Clustering
Connectionist approaches
Function optimization
Global optimization
Issue Date: Feb-2000
Citation: Babu, G.P., Murty, M.N., Keerthi, S.S. (2000-02). A stochastic connectionist approach for global optimization with application to pattern clustering. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 30 (1) : 10-24. ScholarBank@NUS Repository. https://doi.org/10.1109/3477.826943
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. © 2000 IEEE.
Source Title: IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
URI: http://scholarbank.nus.edu.sg/handle/10635/57836
ISSN: 10834419
DOI: 10.1109/3477.826943
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