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Journal of Intelligent Information Systems

, Volume 38, Issue 1, pp 269–297 | Cite as

A density-based spatial clustering for physical constraints

  • Xin Wang
  • Camilo Rostoker
  • Howard J. Hamilton
Article

Abstract

We propose a 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. DBRS+ has been empirically evaluated using synthetic and real data sets and its performance has been compared to DBRS and three related methods for handling obstacles, namely AUTOCLUST+, DBCLuC*, and DBRS_O.

Keywords

Spatial data mining Constraint-based clustering Density-based clustering Obstacle Facilitator 

Notes

Acknowledgements

We thank Vladimir Estivill-Castro and Ickjai Lee for lending us their implementation of AUTOCLUST+ and Jörg Sander for providing us with the code for DBSCAN. We also thank the Regina Police Service and Joe Piwowar for granting us access to the break-and-enter data set. This research was supported by the Faculty of Graduate Studies and Research of the University of Regina and the Natural Sciences and Engineering Research Council of Canada via two Discovery grants to Wang and Hamilton and an Undergraduate Student Research Award to Rostoker.

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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Xin Wang
    • 1
  • Camilo Rostoker
    • 2
  • Howard J. Hamilton
    • 2
  1. 1.Department of Geomatics EngineeringUniversity of CalgaryCalgaryCanada
  2. 2.Department of Computer ScienceUniversity of ReginaReginaCanada

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