Clusterdv: a simple density-based clustering method that is robust, general and automatic.

ABSTRACT:

Motivation

How to partition a dataset into a set of distinct clusters is a ubiquitous and challenging problem. The fact that data vary widely in features such as cluster shape, cluster number, density distribution, background noise, outliers and degree of overlap, makes it difficult to find a single algorithm that can be broadly applied. One recent method, clusterdp, based on search of density peaks, can be applied successfully to cluster many kinds of data, but it is not fully automatic, and fails on some simple data distributions.

Results

We propose an alternative approach, clusterdv, which estimates density dips between points, and allows robust determination of cluster number and distribution across a wide range of data, without any manual parameter adjustment. We show t

SUBMITTER: Marques JC 

PROVIDER: S-EPMC6581440 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature