ST-DBSCAN: An algorithm for clustering spatial-temp oral data


Birant D., Kut R. A.

DATA & KNOWLEDGE ENGINEERING, vol.60, no.1, pp.208-221, 2007 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 60 Issue: 1
  • Publication Date: 2007
  • Doi Number: 10.1016/j.datak.2006.01.013
  • Journal Name: DATA & KNOWLEDGE ENGINEERING
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED)
  • Page Numbers: pp.208-221
  • Keywords: data mining, cluster analysis, spatial-temporal data, cluster visualization, algorithms, DATABASES, DENSITY
  • Dokuz Eylül University Affiliated: Yes

Abstract

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. (c) 2006 Elsevier B.V. All rights reserved.