Fast and eager k-medoids clustering: O(k) runtime improvement of the PAM, CLARA, and CLARANS algorithms
作者：
Highlights：
• Faster k-Medoids (PAM) clustering algorithm.
• Scalable for large number of clusters (large k).
• Sampling-based approximations for large data sets (large n).
• Same quality as previous state-of-the-art techniques (PAM).
• Included in popular clustering tools such as ELKI and R.
摘要
•Faster k-Medoids (PAM) clustering algorithm.•Scalable for large number of clusters (large k).•Sampling-based approximations for large data sets (large n).•Same quality as previous state-of-the-art techniques (PAM).•Included in popular clustering tools such as ELKI and R.
论文关键词：Cluster analysis,k-medoids,PAM,CLARA,CLARANS
论文评审过程：Received 13 March 2020, Revised 7 December 2020, Accepted 17 May 2021, Available online 21 May 2021, Version of Record 29 May 2021.
论文官网地址：https://doi.org/10.1016/j.is.2021.101804
