DENDIS: A new density-based sampling for clustering algorithm

作者：

Highlights：

• ⟨⟨DENDIS⟩⟩ is an hybrid algorithm having the goal to do sampling for clustering.

• It manages both density and distance concepts.

• It is driven by a unique, and meaningful, parameter called granularity.

• It is accurate, fast and parsimonious thanks to internal optimizations.

摘要

•⟨⟨DENDIS⟩⟩ is an hybrid algorithm having the goal to do sampling for clustering.•It manages both density and distance concepts.•It is driven by a unique, and meaningful, parameter called granularity.•It is accurate, fast and parsimonious thanks to internal optimizations.

论文关键词：Density,Distance,Space coverage,Clustering,Rand index

论文评审过程：Received 6 November 2015, Revised 6 February 2016, Accepted 3 March 2016, Available online 17 March 2016, Version of Record 1 April 2016.

论文官网地址：https://doi.org/10.1016/j.eswa.2016.03.008