Skip to main content
Springer Nature Link
Log in

A density-based spatial clustering for physical constraints

  • Published:
Journal of Intelligent Information Systems Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • de Berg, M., van Kreveld, M., Overmars, M., & Schwarzkopf, O. (1997). Computational geometry: Algorithms and applications. Springer.

  • Ester, M., Kriegel, H., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the second international conference on knowledge discovery and data mining (pp. 226–231). Portland, OR.

  • Estivill-Castro, V., & Lee, I. J. (2000a). AUTOCLUST: Automatic Clustering via boundary extraction for mining massive point-data sets. In Proceedings of the fifth international conference on geocomputation (pp. 23–25). Medway Campus, UK: University of Greenwich.

    Google Scholar 

  • Estivill-Castro, V., & Lee, I. J. (2000b). AUTOCLUST+: Automatic clustering of point-data sets in the presence of obstacles. In Proceedings of the International Workshop on Temporal, Spatial and Spatio-Temporal Data Mining (pp. 133–146). Lyon, France.

    Google Scholar 

  • Gao, Y., & Zheng, B. (2009). Continuous obstructed nearest neighbor queries in spatial databases. SIGMOD, 577–590.

  • GeoPinPoint webpage (2002). http://www.dmtispatial.com/geocoding_software.html

  • Lee, C. H. (2002). Density-based clustering of spatial data in the presence of physical constraints. Master’s Thesis. University of Alberta, Edmonton, Canada.

  • Malerba, D., Appice, A., Varlaro, A., & Lanza, A. (2005). Spatial clustering of structured objects. ILP, 227–245.

  • Ng, R., & Han, J. (1994). Efficient and effective clustering method for spatial data mining. In Proceedings of the Twentieth International Conference on Very Large Data Bases (pp. 144–155). Santiago, Chile.

  • O’Rourke, J. (1998). Computational geometry in C (2nd ed.). New York: Cambridge University Press.

    MATH  Google Scholar 

  • Sander, J., Ester, M., Kriegel, H., & Xu, X. (1998). Density-based clustering in spatial databases: the algorithm GDBSCAN and its applications. Data Mining and Knowledge Discovery, 2(2), 169–194.

    Article  Google Scholar 

  • synGeoDataGen webpage (2005). http://www.cs.uregina.ca/~wangx/synGeoDataGen.html

  • Tung, A. K. H. , Han, J., Lakshmanan, L. V. S. , & Ng, R. T. (2001a). Constraint-based clustering in large databases. In Proceedings of the Eighth International Conference on Database Theory (pp. 405–419). London, UK.

  • Tung, A. K. H. , Hou, J., & Han, J. (2001b). Spatial clustering in the presence of obstacles. In Proceedings of the Seventeenth International Conference On Data Engineering (pp. 359–367). Heidelberg, Germany.

  • Wang, X., & Hamilton, H. J. (2003). DBRS: A density-based spatial clustering method with random sampling. In Proceedings of the Seventh Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 563–575). Seoul, Korea.

  • Wang, X., & Hamilton, H. J. (2005). Clustering spatial data in the presence of obstacles. International Journal on Artificial Intelligence Tools, 14(1–2), 177–198.

    Article  Google Scholar 

  • Zaïane, O. R. , & Lee, C. H. (2002). Clustering spatial data when facing physical constraints. In Proceedings of the IEEE International Conference on Data Mining (pp. 737–740). Maebashi City, Japan.

  • Zhang, B., Ying, W., Xie, M., & Dong, J. (2007). Geo-spatial clustering with non-spatial attributes and geographic non-overlapping constraint: a penalized spatial distance measure. PAKDD, 1072–1079.

Download references

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.

Author information

Authors and Affiliations

  1. Department of Geomatics Engineering, University of Calgary, Calgary, AB, T2N 1N4, Canada

    Xin Wang

  2. Department of Computer Science, University of Regina, Regina, SK, S4S 0A2, Canada

    Camilo Rostoker & Howard J. Hamilton

Authors
  1. Xin Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Camilo Rostoker
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Howard J. Hamilton
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Xin Wang.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wang, X., Rostoker, C. & Hamilton, H.J. A density-based spatial clustering for physical constraints. J Intell Inf Syst 38, 269–297 (2012). https://doi.org/10.1007/s10844-011-0154-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10844-011-0154-7

Keywords