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A Clustering Method for Large Spatial Databases

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Computational Science and Its Applications – ICCSA 2004 (ICCSA 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3044))

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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.

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References

  1. Bailey, T.C., Gatrell, A.C.: Interactive spatial data analysis. Addison-Wesley Longman, Edinburgh (1995)

    Google Scholar 

  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. 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. 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. Koperski, K., Han, J., Adhikary, J.: Mining Knowledge in Geographical Data (1998), ftp://ftp.fas.sfu.ca/pubcs/han/pdf/geosurvey98.pdf

  6. Prestamburgo, M.: La classificazionedegli ambiti agricoli: una proposta metodologica (1981)

    Google Scholar 

  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. Schoier, G.: Blockmodeling techniques for Web Mining. In: Procedings in Computational Statistics, COMPSTAT 2002, pp. 201–206. Springer, Berlin (2002)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Dipartimento di Scienze Economiche e Statistiche, Universitá di Trieste, Piazzale Europa 1, 34127, Trieste, Italia

    Gabriella Schoier

  2. Dipartimento di Scienze Geografiche e Storiche, Universitá di Trieste, Centro d’ecellenza in Telegeomatica-GeoNetLab, Piazzale Europa 1, 34127, Trieste, Italia

    Giuseppe Borruso

Authors
  1. Gabriella Schoier
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  2. Giuseppe Borruso
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Editor information

Editors and Affiliations

  1. Department of Chemistry, University of Perugia, Via Elce di Sotto, 8, I-06123, Perugia, Italy

    Antonio Laganá

  2. Department of Computer Science, University of Calgary, 2500 University Drive N.W., T2N 1N4, Calgary AB, Canada

    Marina L. Gavrilova

  3. William Norris Professor, Head of the Computer Science and Engineering Department, University of Minnesota, USA

    Vipin Kumar

  4. School of Computing, Soongsil University, Seoul, Korea

    Youngsong Mun

  5. OptimaNumerics Ltd., Cathedral House, 23-31 Waring Street, BT1 2DX, Belfast, UK

    C. J. Kenneth Tan

  6. Department of Mathematics and Computer Science, University of Perugia, via Vanvitelli, 1, I-06123, Perugia, Italy

    Osvaldo Gervasi

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© 2004 Springer-Verlag Berlin Heidelberg

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Schoier, G., Borruso, G. (2004). A Clustering Method for Large Spatial Databases. In: Laganá, A., Gavrilova, M.L., Kumar, V., Mun, Y., Tan, C.J.K., Gervasi, O. (eds) Computational Science and Its Applications – ICCSA 2004. ICCSA 2004. Lecture Notes in Computer Science, vol 3044. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24709-8_114

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  • DOI: https://doi.org/10.1007/978-3-540-24709-8_114

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22056-5

  • Online ISBN: 978-3-540-24709-8

  • eBook Packages: Springer Book Archive

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