Accelerating Density Peak Clustering Algorithm

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 al...

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Veröffentlicht in:Symmetry (Basel) Jg. 11; H. 7; S. 859
1. Verfasser: Lin, Jun-Lin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.07.2019
Schlagworte:
Algorithms
Clustering
Data points
Datasets
Density
Mathematical analysis
Methods
Neighborhoods
Separation
ISSN:2073-8994, 2073-8994
Online-Zugang:Volltext
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:2073-8994
2073-8994
DOI:10.3390/sym11070859