Simulated annealing for selecting optimal initial seeds in the K-means algorithm
G. Phanendra Babu, M. Ramakrishna Murty · NOT FOUND REPOSITORY (Indian Institute of Science Bangalore) · 1994
Explores 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 order to obtain an optimal partition, a search is undertaken to locate optimal initial seeds, such that the K-means algorithm converges to optimal partition. Search space involved in this process is continuous, so discretization is done and simulated annealing is employed for locating optimal initial seeds. Experimental results substantiate the proposed method. Results obtained with the selected data sets are presented.