Fast Particle Swarm optimization for Balanced Clustering
Meng Zhang, Yao Xian Xiao, Xiaoling Song, Xiangguang Dai, Nian Zhang · 2021
There are balanced priorities in various engineering fields (e.g. medicine, statistics, artificial intelligence, and economics, etc.). Some clustering algorithms cannot maintain the natural balanced structure of data. This paper proposes a soft-balanced clustering framework, which can achieve a balanced clustering for each cluster. The model can be formulated d as a mixed-integer optimization problem. We transform the problem into several subproblems and utilize PSO to search the global solution. Experiments show that the proposed algorithm can achieve satisfactory clustering results than other clustering algorithms.