Erratum: Discovering significant patterns (Machine Learning (2007) 68 (1-33) DOI:10.1007/s10994-007-5006-x);Discovering significant patterns

Machine Learning

Volumn 68, Issue 1, 2007, Pages 1-33

  Webb, Geoffrey I ^a  

^a MONASH UNIVERSITY   (Australia)

Author keywords

Association rules; Pattern discovery; Statistical evaluation

Indexed keywords

ASSOCIATION RULES; ERROR ANALYSIS; RISK ANALYSIS; STATISTICAL METHODS; USER INTERFACES;

PATTERN DISCOVERY; RANDOM DATA; STATISTICAL EVALUATION;

PATTERN RECOGNITION;

EID: 34249653461     PISSN: 08856125     EISSN: 15730565     Source Type: Journal    
DOI: 10.1007/s10994-008-5045-y     Document Type: Erratum

References (43)

1. Agrawal, R., & Srikant, R. (1995). Mining sequential patterns. In Eleventh international conference on data engineering (pp. 3-14). Taipei, Taiwan.
2. Agrawal, R., Imielinski, T., & Swami, A. (1993). Mining associations between sets of items in massive databases. In Proceedings of the 1993 ACM-SIGMOD international conference on management of data (pp. 207-216). Washington, DC.
3. Agresti, A. (1992). A survey of exact inference for contingency tables. Statistical Science, 7(1), 131-153.
4. Aumann, Y., & Lindell, Y. (1999). A statistical theory for quantitative association rules. In Proceedings of the fifth ACM SIGKDD international conference on knowledge discovery and data mining (KDD-99) (pp. 261-270).
5. Bastide, Y., Pasquier, N., Taouil, R., Stumme, G., & Lakhal, L. (2000). Mining minimal non-redundant association rules using frequent closed itemsets. In First international conference on computational logic - CL 2000 (pp. 972-986). Berlin: Springer.
6. Bay, S. D., & Pazzani, M. J. (2001). Detecting group differences: Mining contrast sets. Data Mining and Knowledge Discovery, 5(3), 213-246.
7. Bayardo, R. J., Jr., Agrawal, R., & Gunopulos, D. (2000). Constraint-based rule mining in large, dense databases. Data Mining and Knowledge Discovery, 4(2/3), 217-240.
8. Benjamini, Y., & Hochberg, Y. (1995) Controlling the false discovery rate: A new and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B, 57, 289-300.
9. Benjamini, Y., & Yekutieli, D. (2001) The control of the false discovery rate in multiple testing under dependency. The Annals of Statistics, 29(4), 1165-1188.
10. Brijs, T., Swinnen, G., Vanhoof, K., & Wets, G. (1999). Using association rules for product assortment decisions: A case study. In Knowledge discovery and data mining (pp. 254-260).
11. Brin, S., Motwani, R. & Silverstein, C. (1997). Beyond market baskets: Generalizing association rules to correlations. In J. Peckham (Ed.), SIGMOD 1997, proceedings ACM SIGMOD international conference on management of data (pp. 265-276). New York: ACM.
12. Calders, T., & Goethals, B. (2002). Mining all non-derivable frequent itemsets. In Proceedings of the 6th European conference on principles and practice of knowledge discovery in databases, PKDD 2002 (pp. 74-85). Berlin: Springer.
13. Dong, G., & Li, J. (1999). Efficient mining of emerging patterns: Discovering trends and differences. In Proceedings of the fifth ACM SIGKDD international conference on knowledge, discovery and data mining (KDD-99) (pp. 15-18). New York: ACM.
14. DuMouchel, W., & Pregibon, D. (2001). Empirical Bayes screening for multi-item associations. In KDD-2001: Proceedings of the seventh ACM SIGKDD international conference on knowledge discovery and data mining (pp. 76-76). New York: ACM.
15. Hettich, S., & Bay, S. D. (2006). The UCI KDD archive. From http://kdd.ics.uci.edu. Irvine, CA: University of California, Department of Information and Computer Science.
16. Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics, 6, 65-70.
17. International Business Machines. (1996). IBM intelligent miner user's guide, version 1, release 1.
18. Jaroszewicz, S., & Simovici, D. A. (2004). Interestingness of frequent itemsets using Bayesian networks as background knowledge. In R. Kohavi, J. Gehrke, & J. Ghosh (Eds.), KDD-2004: Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining (pp. 178-186). New York: ACM.
19. Jensen, D. D., & Cohen, P. R. (2000) Multiple comparisons in induction algorithms. Machine Learning 38(3), 309-338.
20. Johnson, R., (1984). Elementary statistics. Boston: Duxbury.
21. Klösgen, W. (1996). Explora: A multipattern and multistrategy discovery assistant. In U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, & R. Uthurusamy (Eds.), Advances in knowledge discovery and data mining (pp. 249-271). Menlo Park: AAAI.
22. Kuramochi, M., & Karypis, G. (2001). Frequent subgraph discovery. In Proceedings of the 2001 IEEE international conference on data mining (ICDM-01) (pp. 313-320).
23. Liu, B., Hsu, W., & Ma, Y. (1999). Pruning and summarizing the discovered associations. In Proceedings of the fifth ACM SIGKDD international conference on knowledge discovery and data mining (KDD-99) (pp. 125-134). New York: AAAI.
24. Megiddo, N., & Srikant, R. (1998). Discovering predictive association rules. In Proceedings of the fourth international conference on knowledge discovery and data mining (KDD-98) (pp. 27-78). Menlo Park: AAAI.
25. Michalski, R. S. (1983). A theory and methodology of inductive learning. In R. S. Michalski, J. G. Carbonell, & T. M. Mitchell (Eds.), Machine learning: An artificial intelligence approach (pp. 83-129). Berlin: Springer.
26. Newman, D. J., Hettich, S., Blake, C., & Merz, C. J. (2006). UCI repository of machine learning databases [Machine-readable data repository]. University of California, Department of Information and Computer Science, Irvine, CA.
27. Piatetsky-Shapiro, G. (1991). Discovery, analysis, and presentation of strong rules. In G. Piatetsky-Shapiro, J. Frawley (Eds.), Knowledge discovery in databases (pp. 229-248). Menlo Park: AAAI/MIT Press.
28. Quinlan, J. R. (1993). C4.5: Programs for machine learning. San Mateo: Kaufmann.
29. Quinlan, J. R., & Cameron-Jones, R. M. (1995). Oversearching and layered search in empirical learning. In IJCAI'95 (pp. 1019-1024). Los Altos: Kaufmann.
30. Scheffer, T. (1995). Finding association rules that trade support optimally against confidence. Intelligent Data Analysis, 9(4), 381-395.
31. Scheffer, T., & Wrobel, S. (2002). Finding the most interesting patterns in a database quickly by using sequential sampling. Journal of Machine Learning Research, 3, 833-862.
32. Shaffer, J. P. (1995). Multiple hypothesis testing. Annual Review of Psychology, 46, 561-584.
33. Turney, P. D. (2000). Types of cost in inductive concept learning. In Workshop on cost-sensitive learning at the seventeenth international conference on machine, learning (pp. 15-21). Stanford University, CA.
34. Webb, G. I. (1995). OPUS: An efficient admissible algorithm for unordered search. Journal of Artificial Intelligence Research, 3, 431-465.
35. Webb, G. I. (2001). Discovering associations with numeric variables. In Proceedings of the seventh ACM SIGKDD international conference on knowledge discovery and data mining (KDD-2001) (pp. 383-388). New York: The Association for Computing Machinery.
36. Webb, G. I. (2002). Magnum Opus Version 1.3. Software, G.I. Webb & Associates, Melbourne, Australia.
37. Webb, G. I. (2003). Preliminary investigations into statistically valid exploratory rule discovery. In Proceedings of the Australasian data mining workshop (AusDM03) (pp. 1-9). University of Technology, Sydney.
38. Webb, G. I. (2005). Magnum Opus Version 3.0.1. Software, G.I. Webb & Associates, Melbourne, Australia.
39. Webb, G. I. (2006). Discovering significant rules. In Proceedings of the twelfth ACM SIGKDD international conference on knowledge discovery and data mining, KDD-2006, (pp. 434-443). New York: ACM.
40. Webb, G. I., & Zhang, S. (2005). K-optimal rule discovery. Data Mining and Knowledge. Discovery, 10(1), 39-79.
41. Zaki, M. J. (2000). Generating non-redundant association rules. In Proceedings of the sixth ACM SIGKDD international conference on knowledge discovery and data mining (KDD-2000) (pp. 34-43). New York: ACM.
42. Zhang, H., Padmanabhan, B., & Tuzhilin, A. (2004). On the discovery of significant statistical quantitative rules. In Proceedings of the tenth international conference on knowledge discovery and data mining (KDD-2004) (pp. 374-383). New York: ACM.
43. Zheng, Z., Kohavi, R., & Mason, L. (2001). Real world performance of association rule algorithms. In Proceedings of the seventh international conference, on knowledge discovery and data mining (KDD-2001) (pp. 401-406). New York: ACM.