A parameter-free similarity graph for spectral clustering

作者：

Highlights：

• This study introduces a pre-processing step for spectral clustering.

• A parameter-free similarity graph, Density Adaptive Neighborhood (DAN), is proposed.

• DAN works on the data sets with arbitrary shaped clusters and density variations.

• DAN is robust to the number of attributes, geometric distortion and decimation.

• DAN facilitates the use of spectral clustering algorithms in various domains.

摘要

•This study introduces a pre-processing step for spectral clustering.•A parameter-free similarity graph, Density Adaptive Neighborhood (DAN), is proposed.•DAN works on the data sets with arbitrary shaped clusters and density variations.•DAN is robust to the number of attributes, geometric distortion and decimation.•DAN facilitates the use of spectral clustering algorithms in various domains.

论文关键词：Spectral clustering,Similarity graph,k-nearest neighbor,ε-neighborhood,Fully connected graph

论文评审过程：Received 21 November 2014, Revised 24 July 2015, Accepted 28 July 2015, Available online 4 August 2015, Version of Record 27 September 2015.

论文官网地址：https://doi.org/10.1016/j.eswa.2015.07.074