A TABU SEARCH APPROACH TO THE CLUSTERING PROBLEM
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...
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| Format: | article |
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2020
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| Online Access: | https://eprints.kfupm.edu.sa/id/eprint/2549/1/a_tabu_search_approach_to_the_clustering_alsultan_isi_a1995rv43600011.pdf |
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| Summary: | 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. |
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