Statistical mining of interesting association rules

Statistics and Computing

Volumn 18, Issue 2, 2008, Pages 185-194

  Weiß, Christian H ^a  

^a UNIVERSITY OF WÜRZBURG   (Germany)

Author keywords

Association rules; Interestingness measures; Precision; Statistical properties

Indexed keywords

EID: 41549161248     PISSN: 09603174     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11222-007-9047-6     Document Type: Article

References (33)

1. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings 20th International Conference on Very Large Databases, Chile, Santiago, 1994, pp. 487-499
2. Agrawal, R., Srikant, R.: Mining sequential patterns. In: 11th International Conference on Data Engineering (ICDE '95), Taipeh, Taiwan, 1995, pp. 3-14
3. Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large data bases. In: Proceedings of the ACM SIGMOD Conference on Management of Data, Washington, 1993, pp. 207-216
4. Agresti, A.: Categorical Data Analysis. Wiley, New York (1990)
5. Bolton, R.J., Hand, D.J., Adams, N.M.: Determining hit rate in pattern search. In: Hand, et al. (eds.) Pattern Detection and Discovery. Lecture Notes in Artificial Intelligence, vol. 2447, pp. 36-48. Springer, Berlin (2002)
6. Bolton, R.J., Adams, N.M.: An iterative hypothesis-testing strategy for pattern discovery. In: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2003, pp. 49-58
7. Borgelt, C.: Efficient implementations of Apriori and Eclat. In: Proceedings of the IEEE ICDM Workshop on Frequent Itemset Mining Implementations (FIMI'03), Melbourne, FL, USA, 2003
8. Brin, S., Motwani, R., Ullman, J.D., Tsur, S.: Dynamic itemset counting and implication rules for market basket data. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, 1997, pp. 255-264
9. Chen, M.S., Han, J., Yu, P.S.: Data mining: An overview from a data base perspective. IEEE Trans. Knowl. Data Eng. 8, 866-883 (1996)
10. Crowder, M., Sweeting, T.: Bayesian inference for a bivariate binomial distribution. Biometrika 76(3), 599-603 (1989)
11. DuMouchel, W., Prebigon, D.: Empirical Bayes screening for multi-item associations. In: Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2001, pp. 67-76
12. Fienberg, S.E., Slavkovic, A.B.: Preserving the confidentiality of categorical statistical data bases when releasing information for association rules. Data Min. Knowl. Discov. 11, 155-180 (2005)
13. Geng, L., Hamilton, H.J.: Interestingness measures for data mining: a survey. ACM Comput. Surv. 38(3), 1-32 (2006)
14. Goodman, L.A.: On simultaneous confidence intervals for multinomial proportions. Technometrics 7(2), 247-254 (1965)
15. Hahsler, M., Grün, B., Hornik, K.: Introduction to arules-Mining Association rules and frequent item sets. Available at: http://cran2. arsmachinandi.it/doc/vignettes/arules/arules.pdf (2006)
16. Hilderman, R.J., Hamilton, H.J.: Knowledge discovery and interestingness measures: a survey. Technical report TR 99-04, ISBN 0-7731-0391-0, University of Regina, Canada (1999)
17. Hilderman, R.J., Hamilton, H.J.: Evaluation of interestingness measures for ranking discovered knowledge. In: Cheung, et al. (eds.) PAKDD 2001. Lecture Notes in Artificial Intelligence, vol. 2035, pp. 247-259. Springer, Berlin (2001)
18. Hipp, J., Güntzer, U., Nakhaeizadeh, G.: Data mining of association rules and the process of knowledge discovery in data bases. In: Data Mining in E-Commerce, Medicine, and Knowledge Management, 2002, pp. 15-36
19. Kamber, M., Shinghal, R.: Evaluating the interestingness of characteristic rules. In: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD-96), 1996, pp. 263-266
20. Klemettinen, M., Mannila, H., Ronkainen, P., Toivonen, H., Verkamo, A.I.: Finding interesting rules from large sets of discovered association rules. In: Adam, et al. (eds.) 3rd International Conference on Information and Knowledge Management, pp. 401-407. ACM, New York (1997)
21. Mannila, H., Toivonen, H., Verkamo, A.I.: Efficient algorithms for discovering association rules. In: Fayyad, Uthurusamy (eds.) AAAI Workshop on Knowledge Discovery in Databases, Seattle, Washington, 1994, pp. 181-192
22. Mannila, H., Toivonen, H., Verkamo, A.I.: Discovery of frequent episodes in event sequences. Data Min. Knowl. Discov. 1, 259-289 (1997)
23. Megiddo, N., Srikant, R.: Discovering predictive association rules. In: Proceedings of the 4th ACM SIGKDD International Conference on Knowledge Discov. and Data Mining, 1998, pp. 274-278
24. R Development Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. Web: http://www.R-project.org (2006)
25. Shannon, C.: A mathematical theory of communication. Bell Syst. Tech. J. 27(3), 379-423 (1948)
26. Silverstein, C., Brin, S., Motwani, R.: Beyond market baskets: generalizing association rules to dependence rules. Data Min. Knowl. Discov. 2, 39-68 (1998)
27. Smyth, P., Goodman, R.M.: An information theoretic approach to rule induction from databases. IEEE Trans. Knowl. Discov. Data Eng. 4(4), 301-316 (1992)
28. Tan, P.-N., Kumar, V.: Interestingness measures for association patterns: a perspective. Technical report TR00-036, Department of Computer Science, University of Minnesota (2000)
29. Webb, G.I.: Discovering significant rules. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2006, pp. 434-443
30. Weiß, C.H.: Sampling in data mining. In: Ruggeri, F., et al. (eds.) Encyclopedia of Statistics in Quality and Reliability. Wiley, New York (2007, to appear)
31. Zaki, M., Parthasarathy, S., Ogihara, M., Li, W.: New algorithms for fast discovery of association rules. In: Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining (KDD'97), 1997, pp. 283-296
32. Zhang, H., Padmanabhan, B., Tuzhilin, A.: On the discovery of significant statistical quantitative rules. In: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2004, pp. 374-383
33. Zheng, Z., Kohavi, R., Mason, L.: Real world performance of association rule algorithms. In: Proceedings of the 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2001, pp. 401-406. Long version available under http://robotics.stanford.edu/~ronnyk/realWorldAssocLongPaper.pdf