Hybrid methods for fuzzy clustering based on fuzzy c-means and improved particle swarm optimization

作者：

Highlights：

• We present two new hybrids of FCM and improved self-adaptive PSO.

• The methods are based on the FCM–PSO algorithm.

• We use FCM to initialize one particle to achieve better results in less iterations.

• The new methods are compared to FCM–PSO using many real and synthetic datasets.

• The proposed methods consistently outperform FCM–PSO in three evaluation metrics.

摘要

•We present two new hybrids of FCM and improved self-adaptive PSO.•The methods are based on the FCM–PSO algorithm.•We use FCM to initialize one particle to achieve better results in less iterations.•The new methods are compared to FCM–PSO using many real and synthetic datasets.•The proposed methods consistently outperform FCM–PSO in three evaluation metrics.

论文关键词：Fuzzy clustering,Fuzzy c-means,Improved particle swarm optimization,Adaptive weights

论文评审过程：Available online 27 April 2015, Version of Record 15 May 2015.

论文官网地址：https://doi.org/10.1016/j.eswa.2015.04.032