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Open AccessArticle

Accelerating Density Peak Clustering Algorithm

1
Department of Information Management, Yuan Ze University, Taoyuan 32003, Taiwan
2
Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Taoyuan 32003, Taiwan
Symmetry 2019, 11(7), 859; https://doi.org/10.3390/sym11070859
Received: 28 May 2019 / Revised: 23 June 2019 / Accepted: 26 June 2019 / Published: 2 July 2019
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PDF [3730 KB, uploaded 11 July 2019]

Abstract

The Density Peak Clustering (DPC) algorithm is a new density-based clustering method. It spends most of its execution time on calculating the local density and the separation distance for each data point in a dataset. The purpose of this study is to accelerate its computation. On average, the DPC algorithm scans half of the dataset to calculate the separation distance of each data point. We propose an approach to calculate the separation distance of a data point by scanning only the neighbors of the data point. Additionally, the purpose of the separation distance is to assist in choosing the density peaks, which are the data points with both high local density and high separation distance. We propose an approach to identify non-peak data points at an early stage to avoid calculating their separation distances. Our experimental results show that most of the data points in a dataset can benefit from the proposed approaches to accelerate the DPC algorithm. View Full-Text
Keywords: clustering; density-based clustering; density peak clustering; density-based clustering; density peak
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Lin, J.-L. Accelerating Density Peak Clustering Algorithm. Symmetry 2019, 11, 859.

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