An Ensemble Density-based Clustering Method

DOI

10.2991/iske.2007.45How to use a DOI?

Keywords

DBSCAN, Clustering ensemble, Consensus function

Abstract

Density based clustering is sound for its great ability of finding arbitrary shapes of clusters and identifying the number of clusters automatically. DBSCAN is a frequently used density based clustering algorithm. In DBSCAN a density threshold, which is hard to be chosen adaptively, should be specified to determine whether an object is dense or sparse. In this paper we introduce the concept of clustering ensemble to avoid the difficulty of selecting a single appropriate threshold. Performing DBSCAN multiple times with diverse thresholds picked up from a pre-constructed interval, the final partition can be figured out via a consensus function. Experimental results show that this method can go beyond DBSCAN both in validity and stability, and avoid the inefficiency caused by any inappropriate thresholds.

Copyright

© 2007, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Luning Xia
AU  - Jiwu Jing
PY  - 2007/10
DA  - 2007/10
TI  - An Ensemble Density-based Clustering Method
BT  - Proceedings of the 2007 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2007)
PB  - Atlantis Press
SP  - 263
EP  - 269
SN  - 1951-6851
UR  - https://doi.org/10.2991/iske.2007.45
DO  - 10.2991/iske.2007.45
ID  - Xia2007/10
ER  -