Clustering Based on Sequential Multi-Objective Games

Abstract

We propose a novel approach for data clustering based on sequential multi-objective multi-act games (ClusSMOG). It automatically determines the number of clusters and optimises simultaneously the inertia and the connectivity objectives. The approach consists of three structured steps. The first step identifies initial clusters and calculates a set of conflict-clusters. In the second step, for each conflict-cluster, we construct a sequence of multi-objective multi-act sequential two-player games. In the third step, we develop a sequential two-player game between each cluster representative and its nearest neighbour. For each game, payoff functions corresponding to the objectives were defined. We use a backward induction method to calculate Nash equilibrium for each game. Experimental results confirm the effectiveness of the proposed approach over state-of-the-art clustering algorithms.