A simulated annealing algorithm with a dual perturbation method for clustering

作者：

Highlights：

• Existing partitional clustering algorithms still settle upon local optima.

• We propose a new simulated annealing algorithm with two perturbation methods.

• We compare our algorithm with existing simulated annealing clustering algorithms.

• We show our new algorithm produces clusters of higher quality more consistently.

摘要

•Existing partitional clustering algorithms still settle upon local optima.•We propose a new simulated annealing algorithm with two perturbation methods.•We compare our algorithm with existing simulated annealing clustering algorithms.•We show our new algorithm produces clusters of higher quality more consistently.

论文关键词：Partitional clustering,Simulated annealing,Sum of squared error criterion,K-means

论文评审过程：Received 2 February 2020, Revised 11 October 2020, Accepted 21 October 2020, Available online 22 October 2020, Version of Record 30 January 2021.

论文官网地址：https://doi.org/10.1016/j.patcog.2020.107713