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