A clustering algorithm based on emotional preference and migratory behavior

Abstract

In this paper, a clustering algorithm based on emotional preference and migratory behavior (EPMC) is proposed for data clustering. The algorithm consists of four models: the migration model, the emotional preference model, the social group model and the inertial learning model. First, the migration model calculates the probability of individuals being learned, so that individuals can learn from the superior. Second, the emotional preference model is introduced to help individuals find the most suitable neighbor for learning. Third, the social group model divides the whole population into different groups and enhances the mutual cooperation between individuals under different conditions. Finally, the inertial learning model balances the exploration and exploitation during the optimization, so that the algorithm can avoid falling into the local optimal solution. In addition, the convergence of EPMC algorithm is verified by theoretical analysis, and the algorithm is compared with four clustering algorithms. Experimental results validate the effectiveness of EPMC algorithm.