Density-based spatial clustering in the presence of obstacles and facilitators

Authors:
Xin Wang;Camilo Rostoker;Howard J. Hamilton
Affiliations:
University of Regina, Regina, SK, S4S 0A2, Canada;University of Regina, Regina, SK, S4S 0A2, Canada;University of Regina, Regina, SK, S4S 0A2, Canada
Venue:
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Year:
2004

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Abstract

In this paper, we propose a new spatial clustering method, called DBRS+, which aims to cluster spatial data in the presence of both obstacles and facilitators. It can handle datasets with intersected obstacles and facilitators. Without preprocessing, DBRS+ processes constraints during clustering. It can find clusters with arbitrary shapes and varying densities. DBRS+ has been empirically evaluated using synthetic and real data sets and its performance has been compared to DBRS, AUTOCLUST+, and DBCLuC*.