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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Summary: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 algorithms, our algorithm has the ability of discovering clusters according to non-spatial, spatial and temporal values of the objects. In this paper, we also present a spatial–temporal data warehouse system designed for storing and clustering a wide range of spatial–temporal data. We show an implementation of our algorithm by using this data warehouse and present the data mining results.
ISSN:0169-023X
1872-6933
DOI:10.1016/j.datak.2006.01.013