An Effective Clustering Method for Finding Density Peaks

Authors

Rui-dong Qi, Inner Mongolia UniversityFollow
Jian-Tao ZhouFollow
Xiaoyu Song, Portland State UniversityFollow

Published In

2018 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Ubiquitous Computing & Communications, Big Data & Cloud Computing, Social Computing & Networking, Sustainable Computing & Communications (ISPA/IUCC/BDCloud/SocialCom/SustainCom)

Document Type

Citation

Publication Date

3-21-2019

Abstract

Unsupervised clustering algorithm is successfully applied in many fields. While the method of fast search and find of density peaks can efficiently discover the centers of clusters by finding the high-density peaks, it suffers from selecting the cluster center manually which depends legitimately on subjective experience. This paper presents a novel effective clustering method for finding density peaks (ECDP). We harness statistics-based methods with geometric features to attain the density peaks automatically and accurately. Our studies demonstrate that our approach can select the cluster center efficiently and effectively for massive datasets.

Description

© Copyright 2019 IEEE - All rights reserved.

Locate the Document

http://doi.org/10.1109/BDCloud.2018.00020

DOI

10.1109/BDCloud.2018.00020

Persistent Identifier

https://archives.pdx.edu/ds/psu/29048

Citation Details

R. Qi, J. Zhou and X. Song, "An Effective Clustering Method for Finding Density Peaks," 2018 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Ubiquitous Computing & Communications, Big Data & Cloud Computing, Social Computing & Networking, Sustainable Computing & Communications (ISPA/IUCC/BDCloud/SocialCom/SustainCom), Melbourne, Australia, 2018, pp. 39-46.