Publication Type

Conference Paper

Version

acceptedVersion

Publication Date

12-2017

Abstract

The density peak clustering (DPC) algorithm is designed to quickly identify intricate-shaped clusters with high dimensionality by finding high-density peaks in a non-iterative manner and using only one threshold parameter. However, DPC has certain limitations in processing low-density data points because it only takes the global data density distribution into account. As such, DPC may confine in forming low-density data clusters, or in other words, DPC may fail in detecting anomalies and borderline points. In this paper, we analyze the limitations of DPC and propose a novel density peak clustering algorithm to better handle low-density clustering tasks. Specifically, our algorithm provides a better decision graph comparing to DPC for the determination of cluster centroids. Experimental results show that our algorithm outperforms DPC and other clustering algorithms on the benchmarking datasets.

Keywords

clustering, density peak clustering, squared residual error, low-density data points

Discipline

Databases and Information Systems | Theory and Algorithms

Research Areas

Data Science and Engineering

Publication

2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC), Shenzhen, China, December 15-18

Identifier

10.1109/SPAC.2017.8304248

Publisher

Springer, Cham

City or Country

Shenzhen, China

Additional URL

https://doi.org/10.1109/SPAC.2017.8304248

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