Clustering boundary detection for high dimensional space based on space inversion and Hopkins statistics

作者：

Highlights：

• Propose a high dimensional space inversion technique to extract the local space features.

• Propose a Symmetry Statistics to describe the uniformity of high dimensional data space.

• Propose a clustering boundary detection algorithm for high dimensional data space named Spinver.

摘要

•Propose a high dimensional space inversion technique to extract the local space features.•Propose a Symmetry Statistics to describe the uniformity of high dimensional data space.•Propose a clustering boundary detection algorithm for high dimensional data space named Spinver.

论文关键词：Clustering boundary,High dimensional space,Space inversion,Symmetry Statistics

论文评审过程：Received 21 October 2015, Revised 29 December 2015, Accepted 26 January 2016, Available online 3 February 2016, Version of Record 9 March 2016.

论文官网地址：https://doi.org/10.1016/j.knosys.2016.01.035