ST-DBSCAN: An algorithm for clustering spatial–temporal data

This paper presents a new density-based clustering algorithm, ST-DBSCAN, which is based on DBSCAN. We propose three marginal extensions to DBSCAN related with the identification of (i) core objects, (ii) noise objects, and (iii) adjacent clusters. In contrast to the existing density-based clustering...

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Bibliographic Details

Published in:	Data & knowledge engineering Vol. 60; no. 1; pp. 208 - 221
Main Authors:	Birant, Derya, Kut, Alp
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 2007
Subjects:	Algorithms Cluster analysis Cluster visualization Data mining Spatial–temporal data Cluster analysis Algorithms Spatial–temporal data Data mining Cluster visualization
ISSN:	0169-023X, 1872-6933
Online Access:	Get full text
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