Multi-Objective Clustering Based on Congestion Games With Player-Specific Cost Functions

Cluster analysis, the unsupervised identification of homogeneous groups, has become essential in numerous domains due to its broad applicability. This paper introduces a multi-objective clustering technique based on game theory principles. Specifically, we formulate the clustering problem using Singleton Congestion Games with Player-Specific Cost Functions, a sub-class of non-cooperative simultaneous-move games, where cluster heads act as players and data objects serve as resources. This clustering model is then solved using a solution concept known as Nash equilibrium. This model offers the main advantage of tackling a solution concept in polynomial time, resulting in an overall favorable computational complexity for the proposed algorithm, MOCA-II. Experimental results on diverse synthetic and real-world datasets demonstrate that MOCA-II outperforms both mono-objective and multi-objective state-of-the-art algorithms.