A fast hybrid clustering technique based on local nearest neighbor using minimum spanning tree
作者：
Highlights：
• The computational complexity of the proposed hybrid technique is sub-quadratic.
• Minimum spanning tree is used to identify the nearest neighbor of each data points.
• The concept of dispersion of data points is used for partitioning the datasets into sub-clusters.
• A novel merge index is introduced based on “cohesion” and ”intra similarity”.
• It shows the better performance as compared to popular clustering algorithms.
摘要
•The computational complexity of the proposed hybrid technique is sub-quadratic.•Minimum spanning tree is used to identify the nearest neighbor of each data points.•The concept of dispersion of data points is used for partitioning the datasets into sub-clusters.•A novel merge index is introduced based on “cohesion” and ”intra similarity”.•It shows the better performance as compared to popular clustering algorithms.
论文关键词：Hybrid clustering,Local nearest neighbor,Minimum spanning tree,Dispersion,Merge index,Gene expression dataset
论文评审过程：Received 19 December 2018, Revised 19 April 2019, Accepted 19 April 2019, Available online 20 April 2019, Version of Record 6 May 2019.
论文官网地址：https://doi.org/10.1016/j.eswa.2019.04.048
