AlSultan, K.S. A TABU SEARCH APPROACH TO THE CLUSTERING PROBLEM. PATTERN RECOGNITION, 28. pp. 1443-1451.
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.
|Divisions:||College Of Computer Sciences and Engineering > Systems Engineering Dept|
|Deposited By:||SHAIKH ARIFUSALAM|
|Deposited On:||14 Jun 2008 13:37|
|Last Modified:||12 Apr 2011 13:13|
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