A Tabu Search Approach to Clustering

In Clustering Problems, groups of similar subjects are to be retrieved from large data sets. Meta-heuristics are often used to obtain high quality solutions within reasonable time limits. Tabu search has proved to be a successful methodology for solving optimization problems, but applications to clustering problems are rare. In this paper, we construct a tabu search approach and compare it to the existing k-means and simulated annealing approaches. We find that tabu search returns solutions of very high quality for various types of cluster instances