A stochastic connectionist approach for global optimization with application to pattern clustering

• Published in: IEEE transactions on systems, man and cybernetics. Part B, Cybernetics Vol. 30; no. 1; pp. 10 - 24
• Main Authors: Babu, G.P., Murty, N.M., Keerthi, S.S.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.02.2000
• Subjects: Clustering; Clustering algorithms; Criteria; Cybernetics; Extraterrestrial measurements; Fuzzy sets; Hardware; Optimization; Partitioning algorithms; Pattern analysis; Pattern clustering; Robustness; Simulated annealing; Stochastic processes; Stochasticity
• ISSN: 1083-4419; 1941-0492
• DOI: 10.1109/3477.826943

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.