Screening and interpreting multi-item associations based on log-linear modeling

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

Volumn , Issue , 2003, Pages 276-285

  Wu, Xintao ^a   Barbará, Daniel ^b   Ye, Yong ^a  

^a UNIVERSITY OF NORTH CAROLINA AT CHARLOTTE   (United States)

^b GEORGE MASON UNIVERSITY   (United States)

Author keywords

Association rule; Graphical model; Log linear model

Indexed keywords

ASSOCIATION PATTERNS; CLASSICAL APPROACH; DATA MINING COMMUNITY; DATA SETS; GRAPHICAL MODEL; HIGHER ORDER; LARGE DATASETS; LINEAR MODELING; LOGLINEAR MODEL; MULTI-ITEM; SYNTHETIC DATASETS; THEORETICAL RESULT;

ASSOCIATION RULES; DATA MINING; GRAPHIC METHODS; REGRESSION ANALYSIS;

ASSOCIATIVE PROCESSING;

EID: 26944470512     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/956750.956783     Document Type: Conference Paper

References (22)

1. R. Agrawal, T. Imilienski, and A. Swami. Mining association rules between sets of items in large databases. In Proceedings of the ACM SIGMOD International Conference on Management of Database, pages 207-216, 1993.
2. R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In Proceedings of the International Conference on Very Large Data Bases, pages 487-499, September 1994.
3. A. Agresti. Categorical data anlysis. Wiley, 1990.
4. J. Badsberg. An environment for graphical models. Ph. D. Thesis, Aalborg University, Demark, 1995.
5. D. Barbará, W. DuMouchel, C. Faloutsos, P. Hass, J. M. Hellerstein, Y. Ioannidis, H. Jagadish, T. Johnson, R. Ng, V. Poosala, K. Ross, and K. Sevcik. The new jersey data reduction report. Bulletin of the Technical Committee on Data Engineering, 20 (4):3-45, December 1997.
6. D. Barbará and X. Wu. Loglinear based quasi cubes. Journal of Information and Intelligent Systems, 16 (3):255-276, 2001.
7. J. Benedetti and M. Brown. Stratigies for the selection of loglinear models. Biometrics, 34:680-686, 1978.
8. Y. M. Bishop, S. E. Fienberg, and P. W. Holland. Discrete Multivariate Analysis: Theory and Practice. The MIT Press, Cambride, Massachusetts, and London, England, 1975.
9. M. Brown. Screening effects in multidimensional contigency tables. Applied Statistics, 25:37-46, 1976.
10. COIL challenge 2000. The insurance company (tic) benchmark. http://kdd.ics.uci.edu/databases/tic/tic.html.
11. A. Deshpande, M. Garofalakis, and R. Rastogi. Independence is good: Dependency-based histogram synopses for high-dimensional data. In Proceedings of the 2001 ACM SIGMOD International Conference on Management of Data, pages 199-210. Santa Barbara, California, May 2001.
12. W. DuMouchel and D. Pregibon. Empirical bayes screening for multi-item association. In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data. San Francisco, CA, August 2001.
13. L. Goodman. The analysis of multidimensional contigency tables: Stepwise procedures and direct estimation methods for building models for multiple classifications. Technometrics, 13:33-61, 1971.
14. J. Han, J. Pei, and Y. Yin. Mining frequent patterns without candidate generation. In Proceedings of the ACM SIGMOD International Conference on Management of Database, pages 1-12. Dallas, TX, May 2000.
15. V. Harinarayan, A. Rajaraman, and J. Ullman. Implementing data cubes efficiently. In Proceedings of the ACM SIGMOD International Conference on Management of Database, pages 205-216. Montreal, Quebec, Canada, 1996.
16. S. Lauritzen. Graphical Models. Oxford University Press, 1996.
17. D. Pavlov, H. Mannila, and P. Symth. Probabilistic models for query approximation with large sparse binary data sets. In Proceedings of Uncertainty in Artificial Intelligence, pages 199-210. Stanford, California, June 2000.
18. S. Sarawagi, R. Agrawal, and N. Meggido. Discovery-driven exploration of olap data cubes. In Proceedings of the International Conference on Extending Data Base Technolgy, pages 168-182. Valencia, Spain, 1998.
19. C. Silverstein, S. Brin, and R. Motwani. Beyond market baskets: Generalizing association rules to dependence rules. Data Mining and Knowledge Discovery, 2:39-68, 1998.
20. R. Tarjan. Decomposition by clique separators. Discrete Mathematics, 55:221-232, 1985.
21. J. Whittaker. Graphical Models in Applied Mathematical Multivariate Statistics. Wiley, 1990.
22. X. Wu and D. Barbará. Modeling and imputation of large incomplete multidimensional datasets. In Proceedings of the International Conference on Data Warehousing and Knowledge Discovery. Aix-en-Provence, France, Septemeber 2002.