SIMULATED ANNEALING FOR SELECTING OPTIMAL INITIAL SEEDS IN THE K-MEANS ALGORITHM

In this paper, we explore the applicability of simulated annealing, a probabilistic search method, for finding optimal partition of the data . A new formulation of the clustering problem is investigated. In orde r to obtain optimal partition, search is undertaken to locate optimal initial seeds, such that the K-means algorithm converges to optimal pa rtition. Search space involved in this process is continuous, so decre tization is done and simulated annealing is employed for locating opti mal initial seeds. Experimental results substantiate the proposed meth od. Results obtained with the selected data sets are presented.