Advertisement

A Clustering Method for Large Spatial Databases

  • Gabriella Schoier
  • Giuseppe Borruso
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3044)

Abstract

The rapid developments in the availability and access to spatially referenced information in a variety of areas, has induced the need for better analysis techniques to understand the various phenomena. In particular spatial clustering algorithms which groups similar spatial objects into classes can be used for the identification of areas sharing common characteristics. The aim of this paper is to present a density-based algorithm for the discover of clusters in large spatial data set which is a modification of a recently proposed algorithm.This is applied to a real data set related to homogeneous agricultural environments.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bailey, T.C., Gatrell, A.C.: Interactive spatial data analysis. Addison-Wesley Longman, Edinburgh (1995)Google Scholar
  2. 2.
    Ester, M., Kriegel, H.P., Sander, J., Xiaowei, X.: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In: Proceeding of the 2nd International Confererence on Knowledge Discovery and Data Mining, pp. 94–99 (1996)Google Scholar
  3. 3.
    Fayyad, U., Piatesky -Shapiro, G., Smyth, P.: From Data Mining to Knowledge Discovery in Databases (1996), http://www.kdnuggets.com/gpspubs/aimag-kdd-overview-1996-fayyad.pdf
  4. 4.
    Han, J., Kamber, M., Tung, A.K.H.: Spatial Clutering Methods in Data Mining: A Survey (2001), ftp://ftp.fas.sfu.ca/pub/cs/han/pdf/gkdbk01.pdf
  5. 5.
    Koperski, K., Han, J., Adhikary, J.: Mining Knowledge in Geographical Data (1998), ftp://ftp.fas.sfu.ca/pubcs/han/pdf/geosurvey98.pdf
  6. 6.
    Prestamburgo, M.: La classificazionedegli ambiti agricoli: una proposta metodologica (1981)Google Scholar
  7. 7.
    Sander, J., Ester, M., Kriegel, H.: P.,Xiaowei, X.: Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and its applications (1999), http://www.dbs.informatik.uni-muenchen.de/Publikationen/
  8. 8.
    Schoier, G.: Blockmodeling techniques for Web Mining. In: Procedings in Computational Statistics, COMPSTAT 2002, pp. 201–206. Springer, Berlin (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Gabriella Schoier
    • 1
  • Giuseppe Borruso
    • 2
  1. 1.Dipartimento di Scienze Economiche e StatisticheUniversitá di TriesteTriesteItalia
  2. 2.Dipartimento di Scienze Geografiche e StoricheUniversitá di Trieste, Centro d’ecellenza in Telegeomatica-GeoNetLabTriesteItalia

Personalised recommendations