DensityCut: an efficient and versatile topological approach for automatic clustering of biological data.

ABSTRACT:

Motivation

Many biological data processing problems can be formalized as clustering problems to partition data points into sensible and biologically interpretable groups.

Results

This article introduces densityCut, a novel density-based clustering algorithm, which is both time- and space-efficient and proceeds as follows: densityCut first roughly estimates the densities of data points from a K-nearest neighbour graph and then refines the densities via a random walk. A cluster consists of points falling into the basin of attraction of an estimated mode of the underlining density function. A post-processing step merges clusters and generates a hierarchical cluster tree. The number of clusters is selected from the most stable clustering in the hierarchical cluster tree. Experim

SUBMITTER: Ding J 

PROVIDER: S-EPMC5013902 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature