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.

本文提出了一种基于情绪偏好和迁徙行为的聚类算法（EPMC）进行数据聚类。该算法包括四个模型：迁移模型，情绪偏好模型，社会群体模型和惯性学习模型。首先，迁移模型计算个人被学习的概率，以便个人可以向上级学习。其次，引入情感偏好模型来帮助个人找到最适合学习的邻居。第三，社会群体模型将整个人口分为不同的群体，并增强了不同条件下个人之间的相互合作。最后，惯性学习模型在优化过程中平衡了探索和开发，这样该算法可以避免陷入局部最优解。另外，通过理论分析验证了EPMC算法的收敛性，并与四种聚类算法进行了比较。实验结果验证了EPMC算法的有效性。