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In this paper, we approach the classical problem of clustering using solutionconcepts from cooperative game theory such as Nucleolus and Shapley value. Weformulate the problem of clustering as a characteristic form game and develop anovel algorithm DRAC (Density-Restricted Agglomerative Clustering) forclustering. With extensive experimentation on standard data sets, we comparethe performance of DRAC with that of well known algorithms. We show aninteresting result that four prominent solution concepts, Nucleolus, Shapleyvalue, Gately point and \tau-value coincide for the defined characteristic formgame. This vindicates the choice of the characteristic function of theclustering game and also provides strong intuitive foundation for our approach.

Pattern Clustering using Cooperative Game Theory