Robust space transformations for distance-based operations

Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Volumn , Issue , 2001, Pages 126-135

  Knorr, Edwin M ^a   Ng, Raymond T ^a   Zamar, Ruben H ^a  

^a UNIVERSITY OF BRITISH COLUMBIA   (Canada)

Author keywords

Data Mining; Distance based Operations; Outliers; Robust Estimators; Robust Statistics; Space Transformations

Indexed keywords

ALGORITHMS; DATABASE SYSTEMS; MATHEMATICAL TRANSFORMATIONS; RANDOM PROCESSES; RESPONSE TIME (COMPUTER SYSTEMS);

DISTANCE OPERATIONS; RANDOM ALGORITHMS;

DATA MINING;

EID: 0035789577     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/502512.502532     Document Type: Conference Paper

References (27)

1. R,. Agrawal, J. Gehrke, D. Gunopulos and P. Raghavan. Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications. In Proc. 1998 SIGMOD, pp. 94-105.
2. H. Anton and C. Rorres. Elementary Linear Algebra: Applications Version. John Wiley & Sons, 1994.
3. V. Barnett and T. Lewis. Outliers in Statistical Data. 3rd edition. John Wiley & Sons, 1994.
4. N. Beckmann, H.-P. Kriegel, R. Schneider and B. Seeger. The R*-tree: an efficient and robust access method for points and rectangles. In Proc. 1990 SIGMOD, pp. 322-331.
5. K. Bennett, U. Fayyad, and D. Geiger. Density-Based Indexing for Approximate Nearest-Neighbor Queries. In Proc. 1999 SIGKDD, pp. 233-243.
6. T. Bozkaya and M. Ozsoyoglu. Distance-based indexing for high-dimensional metric spaces. In Proc 1997 SIGMOD, pp. 357-368.
7. M. Breunig, H.-P. Kriegel, R. T. Ng, J. Sander. LOF: Identifying Density-Based Local Outliers. In Proc. 2000 SIGMOD, pp. 93-104.
8. G.E.P. Box and D.R. Cox. An Analysis of Transformations (with Discussion). In Journal of the Royal Statistical Society, 26, Series B (Methodological), pp. 211-252, 1964.
9. R. Burden and J. Faires. Numerical Analysis. PWS Publishing, 1993.
10. C. Croux and A. Ruiz-Gazen. A fast algorithm for robust principal components based on projection pursuit. In Prat, A., editor, Compstat: Proceedings in Computational Statistics, Heidelberg: Physica-Verlag, pp. 211-216.
11. M. Ester, H.-P. Kriegel, J. Sander, and X. Xu. A Density-based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In Proc. 1996 KDD, pp. 226-231.
12. C. Faloutsos, R. Barber, M. Flickner, J. Hafner, W. Niblack, D. Petkovic and W. Equitz. Efficient and Effective Querying by Image Content. In: Journal of Intelligent Info. Systems, 3, 4, pp. 231-262, 1994.
13. A. Guttman. R-trees: a dynamic index structure for spatial searching. In Proc. 1984 SIGMOD, pp. 47-57.
14. A. Hinneburg and D. Keim. An efficient approach to clustering in large multimedia databases with noise. In Proc. 1998 KDD, pp. 58-65.
15. I. Jolliffe. Principal Component Analysis. Springer-Verlag, 1986.
16. E. Knorr and R. Ng. Algorithms for Mining Distance-Based Outliers in Large Datasets. In Proc. 1998 VLDB, pp. 392-403.
17. G. Li and Z. Chen. Projection-pursuit approach to robust dispersion matrices and principal components: primary theory and Monte Carlo. In Journal of the American Statistical Association, 80, pp. 759-766.
18. R. Martin and R. Zamar. Bias robust estimation of scale. In The Annals of Statistics, 21, 2, pp. 991-1017, 1993.
19. R. Maronna and V. Yohai. The behaviour of the Stahel-Donoho robust multivariate estimator. In Journal of the American Statistical Association, 90 (429), pp. 330-341, 1995.
20. R. Ng and J. Han. Efficient and Effective Clustering Methods for Spatial Searching, In Proc. 1994 VLDB, pp. 144-155.
21. S. Ramaswamy, R. Rastogi and K. Shim. Efficient Algorithms for Mining Outliers from Large Data Sets. In Proc. 2000 SIGMOD, pp. 427-438.
22. P. Rousseeuw and A. Leroy. Robust Regression and Outlier Detection. John Wiley & Sons, 1987.
23. G. Salton and M. McGill. Introduction to Modern Information Retrieval. McGraw-Hill, 1983.
24. T. Seidl and H.-P. Kriegel. Efficient User-Adaptable Similarity Search in Large Multimedia Databases. In Proc. 1997 VLDB, pp. 506-515.
25. W. A. Stahel. Breakdown of Covariance Estimators. Research report, 31, Fachgruppe fur Statistik, ETH, Zurich, 1981.
26. W. Wang, J. Yang and R. Muntz. STING: A statistical information grid approach to spatial data mining. In Proc. 1997 VLDB, pp. 186-195.
27. V. Yohai and R. Zamar. High breakdown point estimates of regression by means of the minimization of an efficient scale. In Journal of the American Statistical Association, 83 (402), pp. 406-413, 1988.