Clustering using PK-D: A connectivity and density dissimilarity

作者：

Highlights：

• New dissimilarity joining connectivity and density information.

• Clustering using low vector space representation based on the new dissimilarity.

• Interesting clustering application using gene expression and image data.

• Improved clustering quality of simple algorithms like k-means.

摘要

•New dissimilarity joining connectivity and density information.•Clustering using low vector space representation based on the new dissimilarity.•Interesting clustering application using gene expression and image data.•Improved clustering quality of simple algorithms like k-means.

论文关键词：Clustering,Dimensionality reduction

论文评审过程：Received 22 June 2015, Revised 24 November 2015, Accepted 29 December 2015, Available online 7 January 2016, Version of Record 23 January 2016.

论文官网地址：https://doi.org/10.1016/j.eswa.2015.12.037