A novel data clustering algorithm based on gravity center methodology
作者：
Highlights：
• A novel clustering algorithm proposed based on gravity center.
• Uses connectivity and cohesion principles to find the center for every cluster.
• Require no parameters but two coefficients to provide the flexibility.
• 22 experiments were conducted in which 14 synthetic and 8 real healthcare datasets.
• Better clustering quality than other hard partitional clustering algorithms.
摘要
•A novel clustering algorithm proposed based on gravity center.•Uses connectivity and cohesion principles to find the center for every cluster.•Require no parameters but two coefficients to provide the flexibility.•22 experiments were conducted in which 14 synthetic and 8 real healthcare datasets.•Better clustering quality than other hard partitional clustering algorithms.
论文关键词：Algorithm,Cluster analysis,Euclidean distance,Gravity center,Partitional clustering
论文评审过程：Received 16 June 2019, Revised 14 March 2020, Accepted 4 April 2020, Available online 11 May 2020, Version of Record 11 May 2020.
论文官网地址：https://doi.org/10.1016/j.eswa.2020.113435
