Likelihood-based data squashing: A modeling approach to instance construction

Data Mining and Knowledge Discovery

Volumn 6, Issue 2, 2002, Pages 173-190

  Madigan, David ^e   Raghavan, Nandini ^a   Dumouchel, William ^b   Nason, Martha ^c   Posse, Christian ^c   Redgeway, Greg ^d  

^a DoubleClick Inc   (United States)

^b AT AND T LABS RESEARCH   (United States)

^c Talaria Inc   (United States)

^d UNIVERSITY OF WASHINGTON   (United States)

^e NONE

Author keywords

Instance construction, data compression

Indexed keywords

ALGORITHMS; DATA COMPRESSION; REGRESSION ANALYSIS; STATISTICAL METHODS;

LIKELIHOOD-BASED DATA SQUASHING (LDS);

DATA PROCESSING;

EID: 0036100902     PISSN: 13845810     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1014095614948     Document Type: Article

References (15)

1. Aha, D.W., Kilber, D., and Albert, M. K. 1991. Instance-based learning algorithms. Machine Learning, 6:37-66.
2. Box, G.E.P. and Draper, N.R. 1987. Empirical Model Building and Response Surfaces. New York, USA: John Wiley & Sons.
3. Box, C.E.P., Hunter, W.G., and Hunter, J.S. 1978. Statistics for Experimenters: An Introduction to Design, Data Analysis, and Model Building. New York, USA: John Wiley & Sons.
4. Bradley, P.S., Fayyad, U., and Reina, C. 1998, Scaling clustering algorithms to large databases. In Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, pp. 9-15.
5. Breiman, L. and Friedman, J. 1984. Tools for large data set analysis. In Statistical Signal Processing, Edward J. Wegman and James G. Smith (Eds). New York: M. Dekker, pp. 191-197.
6. Catlett, J. Megainduction: A test flight. 1991. In Proceedings of the Eighth International Workshop on Machine Learning, pp. 596-599.
7. DuMouchel, W., Volinsky, C., Johnson, T., Cortes, C., and Pregibon, D. 1999. Squashing flat files flatter. In Proceedings of the Fifth ACM Conference on Knowledge Discovery and Data Mining, pp. 6-15.
8. Furnival, G.M. and Wilson, R.W. 1974. Regression by leaps and bounds. Technometrics, 16: 499-511.
9. Gibson, G.A., Vitter, J.S., and Wilkes, J. 1996. Report of the working group on storage I/O issues in large-scale computing. ACM Computing Surveys, 28:779-793.
10. Lawless, J. and Singhal, K. 1978. Efficient screening of nonnormal regression models. Biometrics, 34, pp. 318-327.
11. Provost, F. and Kolluri, V. 1989. A survey of methods for scaling up inductive algorithms. Journal of Data Mining and Knowledge Discovery, 3:131-169.
12. Schwarz, G. 1978. Estimating the dimension of a model. Annals of Statistics, 6: 461-464.
13. Syed, N.A., Liu, H., and Sung, K.K. 1999. A study of support vectors on model independent example selection. In Proceedings of the Fifth ACM Conference on Knowledge Discovery and Data Mining, pp. 272-276.
14. Venables, W.N. and Ripley, B.D. 1997. Modern Applied Statistics with S-PLUS. New York: Springer-Verlag.
15. Zhang, T., Ramakrishnan, R., and Livny, M. 1996. Birch: An efficient data clustering method for large databases. In Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data, pp. 103-114.