Abstract
Both, the number and the size of spatial databases are rapidly growing because of the large amount of data obtained from satellite images, X-ray crystallography or other scientific equipment. Therefore, automated knowledge discovery becomes more and more important in spatial databases. So far, most of the methods for knowledge discovery in databases (KDD) have been based on relational database systems. In this paper, we address the task of class identification in spatial databases using clustering techniques. We put special emphasis on the integration of the discovery methods with the DB interface, which is crucial for the efficiency of KDD on large databases. The key to this integration is the use of a well-known spatial access method, the R*-tree. The focusing component of a KDD system determines which parts of the database are relevant for the knowledge discovery task. We present several strategies for focusing: selecting representatives from a spatial database, focusing on the relevant clusters and retrieving all objects of a given cluster. We have applied the proposed techniques to real data from a large protein database used for predicting protein-protein docking. A performance evaluation on this database indicates that clustering on large spatial databases can be performed, both, efficiently and effectively.
This research was funded by the German Minister for Research and Technology (BMFT) under grant no. 01 IB 307 B. The authors are responsible for the contents of this paper.
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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.
Bernstein F. C., Koetzle T. F., Williams G. J., Meyer E. F., Brice M. D., Rodgers J. R., Kennard O., Shimanovichi T., Tasumi M.: ‘The Protein Data Bank: a Computer-based Archival File for Macromolecular Structures', Journal of Molecular Biology, Vol. 112, 1977, pp. 535–542.
Brinkhoff T., Horn H., Kriegel H.-P., Schneider R.: «A Storage and Access Architecture for Efficient Query Processing in Spatial Database Systems', Proc. 3rd Int. Symp. on Large Spatial Databases, Singapore, 1993, Lecture Notes in Computer Science, Vol. 692, Springer, pp. 357–376.
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.
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.
Connolly M. L.: ‘Measurement of protein surface shape by solid angles', Journal of Molecular Graphics, Vol. 4, No. 1, 1986, pp. 3–6.
Ester M., Kriegel H.-P., Seidl T., Xu X.: “Shape-based Retrieval of Complementary 3D Surfaces in Protein Databases”, (in German), Proc. GI Conf. on Database Systems for Office Automation, Engineering, and Scientific Applications. 1995, Berlin: Springer 1995.
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.
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.
Han J., Cai Y., Cercone N.: “Data-driven Discovery of Quantitative Rules in Relational Databases”, IEEE Transactions on Knowledge and Data Engineering, Vol. 5, No.1, 1993, pp. 29–40.
Holsheimer M., Kersten M.L.: “Architectural Support for Data Mining”, Proc. AAAI Workshop on Knowledge Discovery in Databases, Seattle, Washington, 1994, pp. 217–228
Kaufman L., Rousseeuw P.J.: “Finding Groups in Data: an Introduction to Cluster Analysis”, John Wiley & Sons, 1990.
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.
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.
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.
Protein Data Bank: ‘Quarterly Newsletter No. 70 (Oct. 1994)', Brookhaven National Laboratory, Upton, NY, 1994.
Preparata F. P., Shamos M. I.: “Computational Geometry”, Springer 1985.
Seidl T., Kriegel H.-P.: ‘Solvent Accessible Surface Representation in a Database System for Protein Docking', Proc. 3rd Int. Conference on Intelligent Systems for Molecular Biology (ISMB-95), Cambridge, UK, AAAI Press, 1995.
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Ester, M., Kriegel, HP., Xu, X. (1995). Knowledge discovery in large spatial databases: Focusing techniques for efficient class identification. In: Egenhofer, M.J., Herring, J.R. (eds) Advances in Spatial Databases. SSD 1995. Lecture Notes in Computer Science, vol 951. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60159-7_5
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DOI: https://doi.org/10.1007/3-540-60159-7_5
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