Abstract
Spatial data mining algorithms heavily depend on the efficient processing of neighborhood relations since the neighbors of many objects have to be investigated in a single run of a typical algorithm. Therefore, providing general concepts for neighborhood relations as well as an efficient implementation of these concepts will allow a tight integration of spatial data mining algorithms with a spatial database management system. This will speed up both, the development and the execution of spatial data mining algorithms. In this paper, we define neighborhood graphs and paths and a small set of database primitives for their manipulation. Furthermore, we introduce neighborhood indices to speed up the processing of our database primitives. We implemented the database primitives on top of a commercial spatial database management system. The effectiveness and efficiency of the proposed approach was evaluated by using an analytical cost model and an extensive experimental study on a geographic database.
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References
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.
Egenhofer M. J.: “Reasoning about Binary Topological Relations”, Proc. 2nd Int. Symp. on Large Spatial Databases, Zurich, Switzerland, 1991, pp. 143–160.
Ester M., Kriegel H.-P., Sander J.: “Spatial Data Mining: A Database Approach”, Proc. 5th Int. Symp. on Large Spatial Databases, Berlin. Germany, 1997, pp. 47–66.
Ester M., Frommelt A., Kriegel H.-P., Sander J.: “Algorithms for Characterization and Trend Detection in Spatial Databases”, Proc. 4th Int. Conf. on Knowledge Discovery and Data Mining, New York City, NY, 1998, pp. 44–50.
Fayyad U. M.,.J., Piatetsky-Shapiro G., Smyth P.: “From Data Mining to Knowledge Discovery: An Overview”, in: Advances in Knowledge Discovery and Data Mining, AAAI Press, Menlo Park, 1996, pp. 1–34.
Gueting R. H.: “An Introduction to Spatial Database Systems”, Special Issue on Spatial Database Systems of the VLDB Journal, Vol. 3, No. 4, October 1994.
Guttman A.: “R-trees: A Dynamic Index Structure for Spatial Searching”, Proc. ACM SIGMOD Int. Conf. on Management of Data, 1984, pp. 47–54.
Illustra Information Technologies, Inc.: “Illustra User’s Guide”, Release 3.3.1 for HP UX 10. 1, 1997.
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.
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.
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.
Lu W., Han J.: “Distance-Associated Join Indices for Spatial Range Search”, Proc. 8th Int. Conf. on Data Engineering, Phoenix, AZ, 1992, pp. 284–292.
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.
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.
Rotem D.: “Spatial Join Indices”, Proc. 7th Int. Conf. on Data Engineering, Kobe, Japan, 1991, pp. 500–509.
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© 1999 Springer-Verlag Berlin Heidelberg
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Ester, M., Gundlach, S., Kriegel, HP., Sander, J. (1999). Database Primitives for Spatial Data Mining. In: Buchmann, A.P. (eds) Datenbanksysteme in Büro, Technik und Wissenschaft. Informatik aktuell. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-60119-4_9
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DOI: https://doi.org/10.1007/978-3-642-60119-4_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-65606-7
Online ISBN: 978-3-642-60119-4
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