Abstract
Spatial data mining recently emerges from a number of real applications, such as real-estate marketing, urban planning, weather forecasting, medical image analysis, road traffic accident analysis, etc. It demands for efficient solutions for many new, expensive, and complicated problems. In this paper, we investigate a proximity matching problem among clusters and features. The investigation involves proximity relationship measurement between clusters and features. We measure proximity in an average fashion to address possible nonuniform data distribution in a cluster. An efficient algorithm, for solving the problem, is proposed and evaluated. The algorithm applies a standard multi-step paradigm in combining with novel lower and upper proximity bounds. The algorithm is implemented in several different modes. Our experiment results do not only give a comparison among them but also illustrate the efficiency of the algorithm.
The work of this author is partially supported by a small ARC
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References
R. Agrawal and R. Srikant, Fast Algorithms for Mining Association Rules, Proceedings of the 20th VLDB Conference, 487–499, 1994.
M. Ankerst, B. Braunmuller, H.-P. Kriegel, T. Seidl, Improving Adaptable Similarity Query Processing by Using Approximations, Proceedings of the 24th VLDB Conference, 206–217, 1998.
W.G. Aref and H. Samet, Optimization Strategies for Spatial Query Processing, Proceedings of the 17th VLDB Conference, 81–90, 1991.
T. Brinkho, H.P. Kriegel, and R. Schneider, and B. Seeger, Multistep processing of spatial joins, Proc. of ACM SIGMOD, pp. 197–208, 1994.
M. Ester, H.-P. Kriegel, J. Sander and X. Xu, A density-based algorithm for discovering clusters in large spatial databases, Proceedings of the Second International Conference on Data Mining KDD-96, 226–231, 1996.
M. Este, H.-P. Kriegel, J. Sander, Spatial Data Mining: A Database Approach, SSD’97, LNCS 1262, 47–65, 1997.
V. Estivill-Castro and A.T. Murray, Discovering Associations in Spatial Data-An Efficient Medoid Based Approach, Proceedings of the Second Pacific-Asia Conference on Knowledge Discovery, LNAI 394, 110–121, 1998.
U. M. Fayyad, S.G. Djorgovski, and N. Weir, Automating the analysis and cataloging of sky surveys, Advances in Knowledge Discovery and Data Mining, AAAI/MIT Press, 1996.
U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, Eds. Advances in Knowledge Discovery and Data Mining, AAAI/MIT Press, Menlo Park, CA, 1996.
D. Fisher, Improving Inference through Conceptual Clustering, Proceedings of 1987 AAAI Conferences, 461–465, 1987.
R.H. Guting, An Introduction to Spatial Database Systems, VLDB Journal, 3(4), 357–400, 1994.
R. Guttman, A Dynamic Index Structure for Spatial Searching, ACM-SIGMOD International Conference on Management of Data, 47–57, 1984.
J. Han, Spatial Data Mining and Spatial Data Warehousing, Invited Talk at SSD’97, 1997.
J. Han, Y. Cai, and N. Cercone, Dynamic Generation and Refinement of Concept Hierarchies for Knowledge Discovery in Databases, IEEE Trans. knowledge and Data Engineering, 5, 29–40, 1993.
J. Han, K. Koperski, and N. Stefanovic, GeoMiner: A System Prototype for Spatial Data Mining, Proceedings of 1997 ACM-SIGMOD International Conference on Management, 553–556, 1997.
G. R. Hjaltason and H. Samet, Incremental Distance Join Algorithms for Spatial Databases, 237–248, Proceedings of the 1998 ACM SIGMOD International Conference on Management of Data, 1998.
L. Kaufman and P.J. Rousseeuw, Finding Groups in Data: an Introduction to Cluster Analysis, John Wiley & Sons, 1990.
E.M. Knorr and R.T. Ng, Finding Aggregate Proximity Relationships and Commonalities in Spatial Data Mining, IEEE Transactions on Knowledge and Data Engineering, 8(6), 884–897, 1996.
E.M. Knorr, R.T. Ng, and D.L. Shilvock, Finding Boundary Shape Matching Relationships in Spatial Data, SSD’97, LNCS 1262, 29–46, 1997.
K. Koperski and J. Han, Discovery of Spatial Association Rules in Geographic Information Databases, Advances in Spatial Databases, Proceeding of 4th Symposium (SSD’95), 47–66, 1995.
K. Koperski, J. Han, and J. Adhikary, Mining Knowledge in Geographic Data, to appear in Communications of ACM.
R.S. Michalski, J.M. Carbonnel, and T.M. Mitchell, editors, Machine Learning: An Artificial Intelligence Approach, Morgan Kaufman, 1983.
X. Lin, X. Zhou, and C. Liu, Efficient Computation of a Proximity Matching in Spatial Databases, Tec. Report, University of New South Wales, 1998.
W. Lu, J. Han, and B.C. Ooi, Knowledge Discovery in Large Spatial Databases, Proceedings of Far East Workshop on Geographic Information Systems, 275–289, 1993.
N. Ng and J. Han, Efficient and Efective Clustering Method for Spatial Data Mining, Proceeding of 1994 VLDB, 144–155, 1994.
J.S. Park, M.-S. Chen, and P.S. Yu, An Efective Hash-Based Algorithm for Mining Association Rules, Proceedings of 1995 ACM SIGMOD, 175–186, 1995.
F. Preparata and M. Shamos, Computational Geometry: An Introduction, SpringerVerlag, New York, 1985.
H. Samet, The Design and Analysis of Spatial Data Structures, Addison-Wesley, 1990.
G. Shaw and D. Wheeler, Statistical Techniques in Geographical Analysis, London, David Fulton, 1994.
H. Toivonen, Sampling Large Databases for Association Rules, Proceedings of 22nd VLDB Conference, 1996.
X. Xu, M. Ester, H.-P. Kriegel, Jorg Sander, A Distribution-Based Clustering Algorithm for Mining in Large Spatial Databases, ICDE’98, 324–331, 1998.
T. Zhang, R. Ramakrishnan and M. Livny, BIRCH: an efficient data clustering method for very large databases, Proceeding of 1996 ACM-SIGMOD International Conference of Management of Data, 103–114, 1996.
X. Zhou, Efficiently Computing Proximity Relationships in Spatial Databases, Master Thesis, University of New South Wales, under preparation, 1999.
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Lin, X., Zhou, X., Liu, C. (1999). Efficiently Matching Proximity Relationships in Spatial Databases. In: Güting, R.H., Papadias, D., Lochovsky, F. (eds) Advances in Spatial Databases. SSD 1999. Lecture Notes in Computer Science, vol 1651. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48482-5_13
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DOI: https://doi.org/10.1007/3-540-48482-5_13
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