A TABU SEARCH APPROACH TO THE CLUSTERING PROBLEM. PATTERN RECOGNITION, 28. pp. 1443-1451.

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Abstract

In this paper we consider the problem of clustering m objects into c clusters. The objects are represented by points in an n-dimensional Euclidean space, and the objective is to classify these m points into c clusters such that the distance between points within a cluster and its center (which is to be found) is minimized. The problem is a nonconvex program that has many local minima. It has been studied by many researchers and the most well-known algorithm for solving it is the k-means algorithm. In this paper, we develop a new algorithm for solving this problem based on a tabu search technique. Preliminary computational experience on the developed algorithm are encouraging and compare favorably with both the k-means and the simulated annealing algorithms.

Item Type:	Article
Subjects:	Computer
Department:	College of Computing and Mathematics > lndustrial and Systems Engineering
Depositing User:	SHAIKH ARIFUSALAM
Date Deposited:	14 Jun 2008 10:37
Last Modified:	01 Nov 2019 13:44
URI:	http://eprints.kfupm.edu.sa/id/eprint/2549