Advertisement

Spatial data mining: A database approach

  • Martin Ester
  • Hans-Peter Kriegel
  • Jörg Sander
Spatial Similarities
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1262)

Abstract

Knowledge discovery in databases (KDD) is an important task in spatial databases since both, the number and the size of such databases are rapidly growing. This paper introduces a set of basic operations which should be supported by a spatial database system (SDBS) to express algorithms for KDD in SDBS. For this purpose, we introduce the concepts of neighborhood graphs and paths and a small set of operations for their manipulation. We argue that these operations are sufficient for KDD algorithms considering spatial neighborhood relations by presenting the implementation of four typical spatial KDD algorithms based on the proposed operations. Furthermore, the efficient support of operations on large neighborhood graphs and on large sets of neighborhood paths by the SDBS is discussed. Neighborhood indices are introduced to materialize selected neighborhood graphs in order to speed up the processing of the proposed operations.

Keywords

Spatial Data Mining Neighborhood Graphs Efficient Query Processing 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [AIS 93]
    Agrawal R., Imielinski T., Swami A.: “Database Mining: A Performance Perspective”, IEEE Transactions on Knowledge and Data Engineering, Vol. 5, No. 6, 1993, pp. 914–925.Google Scholar
  2. [AK 93]
    Agrawal R., Kiernan J.: “An Access Structure for Generalized Transitive Closure Queries”, Proc. 9th Int. Conf. on Data Engineering, 1993, pp. 429–438.Google Scholar
  3. [AS 91]
    Aref W.G., Samet H.: “Optimization Strategies for Spatial Query Processing”, Proc. 17th Int. Conf. VLDB, Barcelona, Spain, 1991, pp. 81–90.Google Scholar
  4. [BC 96]
    Berndt D. J., Clifford J.: “Finding Patterns in Time Series: A Dynamic Programming Approach”, in Fayyad U., Piatetsky-Shapiro G., Smyth P., Uthurusamy R. (eds.): Advances in Knowledge Discovery and Data Mining, AAAI Press/The MIT Press, 1996, pp. 229–248.Google Scholar
  5. [BKSS 90]
    Beckmann N., Kriegel H.-P, Schneider R., Seeger B.: ‘The R*-tree: An Efficient and Robust Access Method for Points and Rectangles', Proc. ACM SIGMOD Int. Conf. on Management of Data, Atlantic City, NJ, 1990, pp. 322–331.Google Scholar
  6. [BKSS 94]
    Brinkhoff T., Kriegel H.-P., Schneider R., Seeger B.: ‘Efficient Multi-Step Processing of Spatial Joins', Proc. ACM SIGMOD Int. Conf. on Management of Data, Minneapolis, MN, 1994, pp. 197–208.Google Scholar
  7. [Chr 68]
    Christaller W.: “Central Places in Southern Germany”, (in German), Wissenschaftliche Buchgesellschaft, 1968.Google Scholar
  8. [Ege 91]
    Egenhofer M. J.: “Reasoning about Binary Topological Relations”, Proc. 2nd Int. Symp. on Large Spatial Databases, Zurich, Switzerland, 1991, pp. 143–160.Google Scholar
  9. [EG 94]
    Erwig M., Gueting R.H.: “Explicit Graphs in a Functional Model for Spatial Databases”, IEEE Transactions on Knowledge and Data Engineering, Vol. 6, No. 5, 1994, pp. 787–803.Google Scholar
  10. [EKX 95]
    Ester M., Kriegel H.-P., Xu X.: “Knowledge Discovery in Large Spatial Databases: Focusing Techniques for Efficient Class Identification”, Proc. 4th Int. Symp. on Large Spatial Databases, Portland, ME, 1995, pp. 67–82.Google Scholar
  11. [EKSX 96]
    Ester M., Kriegel H.-P., Sander J., Xu X.: “A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise”, Proc. 2nd Int. Conf. on Knowledge Discovery and Data Mining, Portland, Oregon, 1996, AAAI Press, 1996.Google Scholar
  12. [FPM 91]
    Frawley W.J., Piatetsky-Shapiro G., Matheus J.: “Knowledge Discovery in Databases: An Overview”, in: Knowledge Discovery in Databases, AAAI Press, Menlo Park, 1991, pp. 1–27.Google Scholar
  13. [IS 89]
    Isaaks E.H., Srivastava R.M.: “Applied Geostatistics”, Oxford University Press, New York, 1989.Google Scholar
  14. [KH 95]
    Koperski K., Han J.: “Discovery of Spatial Association Rules in Geographic Information Databases”, Proc. 4th Int. Symp. on Large Spatial Databases, Portland, ME, 1995, pp. 47–66.Google Scholar
  15. [KHA 96]
    Koperski K., Adhikary J., Han J.: “Knowledge Discovery in Spatial Databases: Progress and Challenges”, Proc. SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, Technical Report 96-08, University of British Columbia, Vancouver, Canada, 1996.Google Scholar
  16. [KN 96]
    Knorr E.M., Ng R.T.: “Finding Aggregate Proximity Relationships and Commonalities in Spatial Data Mining”, IEEE Transactions on Knowledge and Data Engineering, Vol. 8, No. 6, 1996, pp. 884–897.Google Scholar
  17. [LD 89]
    Larson P.-A., Deshpande V.: “A File Structure Supporting Traversal Recursion”, Proc. ACM SIGMOD Int. Conf. on Management of Data, 1989, pp. 243–252.Google Scholar
  18. [LH 92]
    Lu W., Han J.: “Distance-Associated Join Indices for Spatial Range Search”, Proc. 8th Int. Conf. on Data Engineering, Phoenix, Arizona, 1992, pp. 284–292.Google Scholar
  19. [LHO 93]
    Lu W., Han J., Ooi B.C.: “Discovery of General Knowledge in Large Spatial Databases”, Proc. Far East Workshop on Geographic Information Systems, Singapore, 1993, pp. 275–289.Google Scholar
  20. [MCP 93]
    Matheus C.J., Chan P.K., Piatetsky-Shapiro G.: “Systems for Knowledge Discovery in Databases”, IEEE Transactions on Knowledge and Data Engineering, Vol. 5, No. 6, 1993, pp. 903–913.Google Scholar
  21. [Ng 96]
    Ng R.T.: “Spatial Data Mining: Discovering Knowledge of Clusters from Maps”, Proc. SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, Technical Report 96-08, University of British Columbia, Vancouver, Canada, 1996.Google Scholar
  22. [NH 94]
    Ng R.T., Han J.: “Efficient and Effective Clustering Methods for Spatial Data Mining”, Proc. 20th Int. Conf. on Very Large Data Bases, Santiago, Chile, 1994, pp. 144–155.Google Scholar
  23. [Rot 91]
    Rotem D.: “Spatial Join Indices”, Proc. 7th Int. Conf. on Data Engineering, Kobe, Japan, 1991, pp. 500–509.Google Scholar
  24. [Qui 86]
    Quinlan J.R.: Induction of Decision Trees, Machine learning 1, 1986, pp. 81–106.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Martin Ester
    • 1
  • Hans-Peter Kriegel
    • 1
  • Jörg Sander
    • 1
  1. 1.Institute for Computer ScienceUniversity of MunichMuenchenGermany

Personalised recommendations