A novel combinatorial merge-split approach for automatic clustering using imperialist competitive algorithm

作者：

Highlights：

• Improving results by combining random and homogeneity based merge-split method.

• Reducing number of empty clusters by attention to data density for selecting center.

• Avoid falling into local optimum points in the proposed assimilation step.

• State-of-the-art accuracies for solving automatic clustering problems.

摘要

•Improving results by combining random and homogeneity based merge-split method.•Reducing number of empty clusters by attention to data density for selecting center.•Avoid falling into local optimum points in the proposed assimilation step.•State-of-the-art accuracies for solving automatic clustering problems.

论文关键词：Automatic clustering,Evolutionary algorithm,Imperialist competitive algorithm,Homogeneity based merge-split approach,Empty cluster

论文评审过程：Received 3 April 2018, Revised 24 September 2018, Accepted 25 September 2018, Available online 26 September 2018, Version of Record 1 October 2018.

论文官网地址：https://doi.org/10.1016/j.eswa.2018.09.050