Generalizing the notion of confidence

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2005, Pages 402-409

  Steinbach, Michael ^a   Kumar, Vipin ^a  

^a University of Minnesota

Author keywords

[No Author keywords available]

Indexed keywords

ERROR CORRECTION; INFORMATION RETRIEVAL; PROBABILITY; SET THEORY;

ASSOCIATION ANALYSIS; ASSOCIATION PATTERNS; BINARY TRANSACTION DATA;

DATA REDUCTION;

EID: 34547816331     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2005.72     Document Type: Conference Paper

References (22)

1. R. Agrawal, T. Imielinski, and A. N. Swami. Mining association rules between sets of items in large databases. In SIGMOD 93, pages 207-216, Washington, D.C., May 1993.
2. R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In VLDB 94, pages 487-499, 1994.
3. Y. Aumann and Y. Lindell. A statistical theory for quantitative association rules. In KDD99, pages 261-270, 1999.
4. A. Banerjee, S. Merugu, I. S. Dhillon, and J. Ghosh. Clustering with bregman divergences. In SIAM2004, pages 234-245, Lake Buena Vista, FL, April 2004.
5. P. Bollmann-Sdorra, A. Hafez, and V. V. Raghavan. A theoretical framework for association mining based on the boolean retrieval model. In DaWaK 2001, Munich, Germany, pages 21-30, 2001.
6. S. Brin, R. Motwani, and C. Silverstein. Beyond market baskets: generalizing association rules to correlations. In SIGMOD 97, pages 265-276, 1997.
7. J. W. Demmel. Applied Numerical Linear Algebra. SLAM, January 1997.
8. J. Friedman and N. Fisher. Bump-hunting in high-dimensional data. Statistics and Computing, 9(2): 123-143, April 1999.
9. E.-H. Han, G. Karypis, and V. Kumar. Tr# 97-068: Minapriori: An algorithm for finding association rules in data with continuous attributes. Technical report, Department of Computer Science, University of Minnesota, Minneapolis, MN, 1997.
10. T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning. Springer Verlag, August 2001.
11. S. Jarosewicz, D. A. Simovici, and I. Rosenberg. An inclusion-exclusion result for boolean polynomials and its applications in data mining. In Proceedings of the Workshop on Discrete Mathematics and Data Mining (DM&DM), SDM02, Arlington, VA. AAAI, April 2002.
12. R. T. Ng, L. V. S. Lakshmanan, J. Han, and A. Pang. Exploratory mining and pruning optimizations of constrained associations rules. In SIGMOD 98, 1998.
13. M. Okoniewski, L. Gancarz, and P. Gawrysiak. Mining multi-dimensional quantitative associations. In INAP 2001, October, volume 2543 of LCNS. Springer, 2003.
14. A. Ozgur, P.-N. Tan, and V. Kumar. [rba]: An integrated framework for regression based on association rules. In SIAM 2004, April 22-24 2004.
15. R. Srikant and R. Agrawal. Mining quantitative association rules in large relational tables. In SIGMOD 96, 1996.
16. R. Srikant, Q. Vu, and R. Agrawal. Mining association rules with item constraints. In KDD 97, pages 67-73, 1997.
17. M. Steinbach, P.-N. Tan, H. Xiong, and V. Kumar. Generalizing the notion of support. In KDD '04, pages 689-694, New York, NY, USA, 2004. ACM Press.
18. P.-N. Tan, V. Kumar, and J. Srivastava. Selecting the right interestingness measure for association patterns. In KDD 2002, pages 32-41, New York, NY, 2002. ACM Press.
19. P.-N. Tan, M. Steinbach, and V. Kumar. Introduction to Data Mining. Pearson Addison-Wesley, 2005.
20. G. I. Webb. Discovering associations with numeric variables. In KDD 01, pages 383-388, 2001.
21. C. Yang, U. M. Fayyad, and P. S. Bradley. Efficient discovery of error-tolerant frequent itemsets in high dimensions. In KDD 2001, pages 194-203, 2001.
22. M. J. Zaki and M. Ogihara. Theoretical foundations of association rules. In DMKD 98, pages 7:1-7:8, 1998.