Title :
A stochastic connectionist approach for global optimization with application to pattern clustering
Author :
Babu, G. Phanendra ; Murty, M. Narasimha ; Keerthi, S. Sathiya
Author_Institution :
Technol. Deployment Int. Inc., Santa Clara, CA, USA
fDate :
2/1/2000 12:00:00 AM
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
Keywords :
neural nets; pattern clustering; simulated annealing; stochastic processes; K-means algorithm; connectionist approach; function optimization problem; function optimization problems; global optimization; multi-modal functions; parallel hardware; parallelization; partitional clustering criterion; pattern clustering; real-valued parameters; simulated annealing; squared-error criterion; stochastic connectionist approach; stochastic search technique; Clustering algorithms; Extraterrestrial measurements; Fuzzy sets; Hardware; Partitioning algorithms; Pattern analysis; Pattern clustering; Robustness; Simulated annealing; Stochastic processes;
Journal_Title :
Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on
DOI :
10.1109/3477.826943