Connectionist approach for clustering

This paper presents a stochastic connectionist approach for cluster analysis. Clustering problem is formulated as a real-parameter function optimization problem and is solved using the proposed approach. As the proposed connectionist approach performs stochastic search, it avoids getting stuck in a local minimum, and guarantees asymptotic convergence to optimal solution. The amenability of connectionist approaches to massive parallelization enables one to obtain linear speedup with available parallel hardware. Several data sets are clustered using the proposed approach and the partitions obtained are (near) optimal in nature. Results pertaining to some important data sets are presented