An improved K-medoids algorithm based on step increasing and optimizing medoids

作者：

Highlights：

• The proposed clustering algorithm improves performance and preserves efficiency.

• We propose a candidate medoids subset to optimize the clustering medoids.

• We propose increasing the medoid methods in a step-wise fashion.

• Results report better performances than classical methods.

摘要

•The proposed clustering algorithm improves performance and preserves efficiency.•We propose a candidate medoids subset to optimize the clustering medoids.•We propose increasing the medoid methods in a step-wise fashion.•Results report better performances than classical methods.

论文关键词：Clustering analysis,K-medoids,Candidate medoids subset,Optimizing medoids

论文评审过程：Received 3 May 2017, Revised 23 September 2017, Accepted 24 September 2017, Available online 29 September 2017, Version of Record 6 October 2017.

论文官网地址：https://doi.org/10.1016/j.eswa.2017.09.052