Efficiently Identifying Exploratory Rules’ Significance - Data Mining: Theory, Methodology, Techniques, and Applications - page 77

References

1. Rakesh Agrawal, Tomasz Imielinski, and Arun N. Swami. Mining association rules
between sets of items in large databases. In Peter Buneman and Sushil Jajodia,
editors, Proceedings of the 1993 ACM SIGMOD International Conference on Man-
agement of Data , pages 207-216, Washington, D.C., 26-28 1993.

2. Y. Aumann and Y. Lindell. A statistical theory for quantitative association rules.
In , pages 261-270, 1999.

3. S. D. Bay. The uci kdd archive [http://kdd.ics.uci.edu], 1999.
4. S. D. Bay and M. J. Pazzani. Detecting group differences: Mining contrast sets.
In Data Mining and Knowledge Discovery , pages 213-246, 2001.
5. Roberto J. Bayardo, Jr., Rakesh Agrawal, and Dimitrios Gunopulos. Constraint-
based rule mining in large, dense databases. Data Min. Knowl. Discov. , 4(2-3):217-
240, 2000.
6. C. L. Blake and C. J. Merz. UCI repository of machine learning databases, 1998.
7. J. Han and M. Kamber. Data mining : concepts and techniques . Morgan Kaufmann,
2001.
8. Shiying Huang and Geoffrey I. Webb. Discarding insignificant rules during impact
rule discovery in large database. In SIAM Data Mining Conference, 2005, Newport
Beach, USA .
9. Jiawei han Jian Pei and Laks V.S. Lakshmanan. Mining frequent itemsets with
convertible constraints. In Proceedings of the 17th International Conference on
Data Engineering , page 433. IEEE Computer Society, 2001.
10. B. Liu, W. Hsu, and Y. Ma. Pruning and summarizing the discovered associations.
In Knowledge Discovery and Data Mining , pages 125-134, 1999.
11. R. S. Michalski. A theory and methodology of inductive learning. In R. S. Michal-
ski, J. G. Carbonell, and T. M. Mitchell, editors, Machine Learning: An Artificial
Intelligence Approach , pages 83-134. Springer, Berlin, Heidelberg, 1984.
12. J. R. Quinlan. C4.5: programs for machine learning . Morgan Kaufmann Publishers
Inc., 1993.
13. G. I. Webb. Discovering associations with numeric variables. In Proceedings of the
seventh ACM SIGKDD international conference on Knowledge discovery and data
mining , pages 383-388. ACM Press, 2001.
14. Geoffrey I. Webb. OPUS: An e cient admissible algorithm for unordered search.
Journal of Artificial Intelligence Research , 3:431-465, 1995.
15. G. I. Webb. Statistically sound exploratory rule discovery, 2004. To be published.

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