An improved overlapping k-means clustering method for medical applications
作者：
Highlights：
• The sensitivity of overlapping k-means algorithm to initialization is considered.
• The k-harmonic means method is effective for identifying initial cluster centroids.
• The proposed approach outperforms the original overlapping k-means algorithm.
摘要
•The sensitivity of overlapping k-means algorithm to initialization is considered.•The k-harmonic means method is effective for identifying initial cluster centroids.•The proposed approach outperforms the original overlapping k-means algorithm.
论文关键词：Overlapping clustering,Overlapping k-means,K-harmonic means,FBCubed,Data Mining,Medical informatics
论文评审过程：Received 25 May 2016, Revised 25 August 2016, Accepted 14 September 2016, Available online 17 September 2016, Version of Record 22 September 2016.
论文官网地址：https://doi.org/10.1016/j.eswa.2016.09.025
