A comparative study of two density-based spatial clustering algorithms for very large datasets

Authors:
Xin Wang;Howard J. Hamilton
Affiliations:
Department of Computer Science, University of Regina, Regina, SK, Canada;Department of Computer Science, University of Regina, Regina, SK, Canada
Venue:
AI'05 Proceedings of the 18th Canadian Society conference on Advances in Artificial Intelligence
Year:
2005

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Abstract

Spatial clustering is an active research area in spatial data mining with various methods reported In this paper, we compare two density-based methods, DBSCAN and DBRS First, we briefly describe the methods and then compare them from a theoretical view Finally, we give an empirical comparison of the algorithms.