A simple statistics-based nearest neighbor cluster detection algorithm
作者：
Highlights：
• A new clustering algorithm based on simple statistics and lattice metrics is given.
• Mathematical rationale is explained in detail and theorem proofs are provided.
• Performance classification of the SSNN algorithm is illustrated with 2D datasets.
• Jain׳s benchmark dataset is used to show the SSNN cluster finding capability.
• High-dimensional image patterns are included as additional clustering examples.
摘要
Highlights•A new clustering algorithm based on simple statistics and lattice metrics is given.•Mathematical rationale is explained in detail and theorem proofs are provided.•Performance classification of the SSNN algorithm is illustrated with 2D datasets.•Jain׳s benchmark dataset is used to show the SSNN cluster finding capability.•High-dimensional image patterns are included as additional clustering examples.
论文关键词：Clusters,Cluster detection,Clustering,Cluster analysis,Digital geometry,Nearest neighbors,Neighborhoods,Statistics,Pattern recognition
论文评审过程：Received 21 February 2014, Revised 8 August 2014, Accepted 3 October 2014, Available online 29 October 2014.
论文官网地址：https://doi.org/10.1016/j.patcog.2014.10.009
