Clustering and knowledge discovery in spatial databases

Vistas in Astronomy

Volumn 41, Issue 3, 1997, Pages 397-403

  Xu, Xiaowei ^a   Ester, Martin ^a   Kriegel, Hans Peter ^a   Sander, Jörg ^a  

^a UNIVERSITY OF MUNICH   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0012907473     PISSN: 00836656     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0083-6656(97)00044-5     Document Type: Article

References (17)

1. N. Beckmann, H.-P. Kriegel, R. Schneider, B. Seeger, 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) p. 322-331.
2. T. Brinkhoff, H.-P. Kriegel, R. Schneider, B. Seeger, Efficient Multi-Step Processing of Spatial Joins, Proc. ACM SIGMOD Int. Conf. on Management of Data, Minneapolis, MN (1994) p. 197-208.
3. M. Ester, H.-P. Kriegel, J. Sander, X. Xu, 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 (AAAI Press, Menlo Park, 1996).
4. M. Ester, H.-P. Kriegel, X. Xu, Knowledge Discovery in Large Spatial Databases: Focusing Techniques for Efficient Class Identification, Proc. 4th Int. Symp. on Large Spatial Databases, Portland, ME, Lecture Notes in Computer Science, Vol. 951 (Springer, Berlin, 1995) p. 67-82.
5. M. Ester, H.-P. Kriegel, X. Xu, A Database Interface for Clustering in Large Spatial Databases, Proc. 1st Int. Conf. on Knowledge Discovery and Data Mining, Montreal, Canada (AAAI Press, Menlo Park, 1995).
6. U.M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, From Data Mining to Knowledge Discovery: An Overview, In: Advances in Knowledge Discovery and Data Mining (AAAI Press, Menlo Park, 1996) p. 1-34.
7. R.H. Gueting, An Introduction to Spatial Database Systems, The VLDB Journal 3 No. 4 (1994) 357-399.
8. L. Kaufman and P.J. Rousseeuw, Finding Groups in Data: An Introduction to Cluster Analysis (Wiley, New York, 1990).
9. J. MacQueen, Some Methods for Classification and Analysis of Multivariate Observations, 5th Berkeley Symp. Math. Statist. Prob., Vol. 1 (1967) p. 281-297.
10. C.J. Matheus, P.K. Chan, G. Piatetsky-Shapiro, Systems for Knowledge Discovery in Databases, IEEE Trans. on Knowledge and Data Engineering 5 No. 6 (1993) 903-913.
11. F. Murtagh, A Survey of Recent Advances in Hierarchical Clustering Algorithms, The Computer Journal 26 (4) (1983) 354-359.
12. R.T. Ng, J. Han, Efficient and Effective Clustering Methods for Spatial Data Mining, Proc. 20th Int. Conf. on Very Large Data Bases, Santiago, Chile (1994) p. 144-155.
13. J. Nievergelt, H. Hinterberger, K.C. Sevcik, The Grid file: An Adaptable, Symmetric Multikey File Structure, ACM Trans. Database Systems 9 (1) (1984) 38-71.
14. E. Schikuta, Grid Clustering: An Efficient Hierachical Clustering Method for Very Large Data Sets, In Proc. 13th Int. Conf. on Pattern Recognition, Vol. 2, IEEE Computer Society (1996) p. 101-105.
15. R. Sibson, SLINK: an Optimally Efficient Algorithm for the Single-Link Cluster Method, The Computer Journal 16 (1) (1973) 30-34.
16. X. Xu, M. Ester, H.-P. Kriegel, J. Sander, A Nonparametric Clustering Algorithm for Knowledge Discovery in Large Spatial Databases, Submitted to VLDB 97 (1997).
17. T. Zhang, R. Ramakrishnan, M. Linvy, BIRCH: An Efficient Data Clustering Method for Very Large Databases, Proc. ACM SIGMOD Int. Conf. on Management of Data (1996) 103-114.