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Clustering of multiple density peaks

conference contribution
posted on 2018-01-01, 00:00 authored by Borui CaiBorui Cai, Guangyan HuangGuangyan Huang, Yong XiangYong Xiang, J He, Belinda HuangBelinda Huang, K Deng, X Zhou
Density-based clustering, such as Density Peak Clustering (DPC) and DBSCAN, can find clusters with arbitrary shapes and have wide applications such as image processing, spatial data mining and text mining. In DBSCAN, a core point has density greater than a threshold, and can spread its cluster ID to its neighbours. However, the core points selected by one cut/threshold are too coarse to segment fine clusters that are sensitive to densities. DPC resolves this problem by finding a data point with the peak density as centre to develop a fine cluster. Unfortunately, a DPC cluster that comprises only one centre may be too fine to form a natural cluster. In this paper, we provide a novel clustering of multiple density peaks (MDPC) to find clusters with arbitrary number of regional centres with local peak densities through extending DPC. In MDPC, we generate fine seed clusters containing single density peaks, and form clusters with multiple density peaks by merging those clusters that are close to each other and have similar density distributions. Comprehensive experiments have been conducted on both synthetic and real-world datasets to demonstrate the accuracy and effectiveness of MDPC compared with DPC, DBSCAN and other base-line clustering algorithms.

History

Event

Knowledge Discovery and Data Mining. Pacific-Asia Conference (22nd : 2018 : Melbourne, Victoria)

Volume

10939

Series

Lecture Notes in Computer Science

Pagination

413 - 425

Publisher

Springer

Location

Melbourne, Victoria

Place of publication

Cham, Switzerland

Start date

2018-06-03

End date

2018-06-06

ISSN

0302-9743

eISSN

1611-3349

ISBN-13

9783319930398

Language

eng

Publication classification

E Conference publication; E1 Full written paper - refereed

Editor/Contributor(s)

Dinh Phung, Vincent Tseng, Geoffrey Webb, Bao Ho, Mohadeseh Ganji, Lida Rashidi

Title of proceedings

PAKDD 2018 : Advances in Knowledge Discovery and Data Mining : Proceedings of 22nd Pacific-Asia Conference