Evaluation of a clustering technique based on game theory

In this paper we focus on the task of clustering in data mining applications. We introduce a formulation of a new clustering algorithm by modelling the system as a cooperative game in strategic form using game theory. The goal is to partition a dataset into k clusters. Our approach has been applied to both simulated and real-world datasets. In addition, we have implemented functions based on the calculation of errors to track both similarity of the data within the same cluster and dissimilarity measure of the data elements between different clusters. Experimental results show that our algorithm is capable of providing a comprehensive description of the final solutions and it has good predictive capabilities.

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