Multiobjective clustering analysis using particle swarm optimization
作者：
Highlights：
• A multiobjective clustering method based on particle swarm optimization is proposed.
• Two objective functions used to measure cohesion and connectivity of clusters.
• Able to adaptively find the optimal number of clusters.
• Tested on 27 benchmark datasets in terms of accuracy and robustness.
• The system outperformed four state-of-the-art clustering algorithms in most cases.
摘要
•A multiobjective clustering method based on particle swarm optimization is proposed.•Two objective functions used to measure cohesion and connectivity of clusters.•Able to adaptively find the optimal number of clusters.•Tested on 27 benchmark datasets in terms of accuracy and robustness.•The system outperformed four state-of-the-art clustering algorithms in most cases.
论文关键词：Clustering,Multiobjective,Particle swarm optimization
论文评审过程：Received 18 October 2015, Revised 5 February 2016, Accepted 6 February 2016, Available online 16 February 2016, Version of Record 2 March 2016.
论文官网地址：https://doi.org/10.1016/j.eswa.2016.02.009
