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Indexed by:期刊论文

Date of Publication:2008-12-01

Journal:Journal of Computational Information Systems

Included Journals:EI、Scopus

Volume:4

Issue:6

Page Number:2473-2481

ISSN No.:15539105

Abstract:Thanks to their capabilities to handle clusters with arbitrary shape, Density-based clustering algorithms play important roles in various fields such as pattern recognition and so on. However, most Density-based clustering algorithms are sensitive to the parameters, and have difficulties in dealing with various density datasets which appear frequently in reality. In this paper, a novel Density-based clustering algorithm, Density-Tag Based Clustering (DTBC), is proposed. DTBC uses density-tag to automatically detect density distribution of the dataset, thus is effective for various density dataset. Meanwhile, DTBC needs only one parameter to which it is not sensitive. Experimental results show that DTBC is more effective for various density dataset than other typical Density-based clustering algorithms such as DBSCAN and KNNCLUST. © 2008 Binary Information Press.