Publication Type

Journal Article

Version

submittedVersion

Publication Date

7-2019

Abstract

The density peak clustering (DPC) algorithm was designed to identify arbitrary-shaped clusters by finding density peaks in the underlying dataset. Due to its aptitudes of relatively low computational complexity and a small number of control parameters in use, DPC soon became widely adopted. However, because DPC takes the entire data space into consideration during the computation of local density, which is then used to generate a decision graph for the identification of cluster centroids, DPC may face difficulty in differentiating overlapping clusters and in dealing with low-density data points. In this paper, we propose a residual error-based density peak clustering algorithm named REDPC to better handle datasets comprising various data distribution patterns. Specifically, REDPC adopts the residual error computation to measure the local density within a neighbourhood region. As such, comparing to DPC, our REDPC algorithm provides a better decision graph for the identification of cluster centroids and better handles the low-density data points. Experimental results on both synthetic and real-world datasets show that REDPC performs better than DPC and other algorithms.

Keywords

Clustering, Density peak clustering, Anomaly detection, Residual error, Low-density data points

Discipline

Databases and Information Systems | Software Engineering | Theory and Algorithms

Research Areas

Data Science and Engineering

Publication

Neurocomputing

Volume

348

First Page

82

Last Page

96

ISSN

0925-2312

Identifier

10.1016/j.neucom.2018.06.087

Publisher

Elsevier

Additional URL

https://doi.org/10.1016/j.neucom.2018.06.087

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