20.Webb, G.I. (in-press). Self-Sufficient Itemsets: An Approach to Screening Potentially Interesting Associations Between Items. Transactions on Knowledge Discovery from Data. ACM. [Abstract] [Pre-Publication PDF]
19.Novak, P., N. Lavrac, and G.I. Webb (2009). Supervised Descriptive Rule Discovery: A Unifying Survey of Contrast Set, Emerging Pattern and Subgroup Mining. Journal of Machine Learning Research 10., pages 377-403. [Abstract] [Link to paper on JMLR site ]
18.Webb, G.I. (2008). Layered Critical Values: A Powerful Direct-Adjustment Approach to Discovering Significant Patterns. Machine Learning 71(2-3). Netherlands: Springer, pages 307-323 [Technical Note]. [Abstract] [Pre-Publication PDF][Link to paper via Springerlink]
17.Webb, G.I. (2007). Discovering Significant Patterns. Machine Learning 68(1). Netherlands: Springer, pages 1-33. [Abstract] [Pre-publication PDF][Link to paper via Springerlink]
16.Webb, G.I. (2007). Finding the Real Patterns (Extended Abstract). In Zhi-Hua Zhou, Hang Li, Qiang Yang (Ed.), Lecture Notes in Computer Science Vol. 4426 : Advances in Knowledge Discovery and Data Mining Proceedings of the 11th Pacific-Asia Conference, PAKDD 2007 Nanjing, China. Berlin/Heidelberg: Springer, pages 6 ISBN 978-3-540-71700-3.
15.Webb, G.I. (2006). Discovering Significant Rules. In L. Ungar, M. Craven, D. Gunopulos and T. Eliassi-Rad (Eds.), Proceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2006) Philadelphia, PA. New York: The Association for Computing Machinery, pages 434 - 443. [Abstract] [Pre-publication PDF][Download from ACM Portal]
14.Huang, S. and G.I. Webb (2006). Efficiently Identifying Exploratory Rules' Significance. In LNAI State-of-the-Art Survey series, 'Data Mining: Theory, Methodology, Techniques, and Applications'. Berlin/Heidelberg: Springer, pages 64-77, (Note: an earlier version of this paper was published in S.J. Simoff and G.J. Williams (Eds.), Proceedings of the Third Australasian Data Mining Conference (AusDM04) Cairns, Australia. Sydney: University of Technology, pages 169-182.). [Abstract] [Link to paper via Springerlink]
13.Webb, G. I. and S. Zhang (2005). k-Optimal-Rule-Discovery. Data Mining and Knowledge Discovery 10(1). Netherlands: Springer, pages 39-79. [Abstract] [Prepublication PDF][Link to paper via Springerlink]
12.Huang, S. and G.I. Webb (2005). Discarding Insignificant Rules During Impact Rule Discovery in Large, Dense Databases. In H. Kargupta, C. Kamath, J. Srivastava and A. Goodman (Eds.), Proceedings of the Fifth SIAM International Conference on Data Mining (SDM'05) [short paper] Newport Beach, CA. Philadelphia, PA: Society for Industrial and Applied Mathematics, pages 541-545. [Abstract] [Pre-publication PDF][Link to SIAM]
11.Thiruvady, D. R. and G. I. Webb (2004). Mining Negative Rules using GRD. In H. Dai, R. Srikant and C. Zhang (Eds.), Lecture Notes in Computer Science Vol. 3056: Proceedings of the Eighth Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 04) [Short Paper] Sydney, Australia. Berlin/Heidelberg: Springer, pages 161-165. [Abstract] [Pre-publication PDF][Link to paper via Springerlink]
10.Huang, S. and G.I. Webb (2004). Efficiently Identifying Exploratory Rules' Significance. In S.J. Simoff and G.J. Williams (Eds.), Proceedings of the Third Australasian Data Mining Conference (AusDM04) Cairns, Australia. Sydney: University of Technology, pages 169-182. [Abstract] [Pre-publication PDF]
9.Webb, G. I., S. Butler, and D. Newlands (2003). On Detecting Differences Between Groups. In P. Domingos, C. Faloutsos, T. Senator, H. Kargupta and L. Getoor (Eds.), Proceedings of The Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2003) Washington, DC. New York: The Association for Computing Machinery, pages 256-265. [Abstract] [PDF][Paper via ACM Portal]
8.Webb, G.I. (2003). Preliminary Investigations into Statistically Valid Exploratory Rule Discovery. In S.J. Simoff, G.J. Williams and M. Hegland (Eds.), Proceedings of the Second Australasian Data Mining Conference (AusDM03) Canberra, Australia. Sydney: University of Technology, pages 1-9. [Abstract] [Pre-publication PDF]
7.Webb, G. I. and S. Zhang (2002). Removing Trivial Associations in Association Rule Discovery. In Proceedings of the First International NAISO Congress on Autonomous Intelligent Systems (ICAIS 2002) Geelong, Australia. Canada/The Netherlands: NAISO Academic Press. [Abstract] [Pre-publication PDF]
6.Webb, G. I. (2001). Discovering Associations with Numeric Variables. In F. Provost and R. Srikant (Eds.), Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2001)[short paper] San Francisco, CA. New York: The Association for Computing Machinery, pages 383-388. [Abstract] [Pre-publication PDF][Link to paper via ACM Portal]
5.Webb, G.I. and S. Zhang (2001). Further Pruning for Efficient Association Rule Discovery. In M. Stumptner, D. Corbett and M.J. Brooks (Eds.), Lecture Notes in Computer Science Vol. 2256: Proceedings of the 14th Australian Joint Conference on Artificial Intelligence (AI'01) Adelaide, Australia. Berlin: Springer, pages 605-618. [Abstract] [Pre-publication PDF][Link to paper via Springerlink]
4.Webb, G. I. (2000). Efficient Search for Association Rules. In R. Ramakrishnan and S. Stolfo (Eds.), Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2000) Boston, MA. New York: The Association for Computing Machinery, pages 99-107. [Abstract] [Pre-publication PDF][Link to paper via ACM Portal]
3.Webb, G. I. (1996). Inclusive Pruning: A New Class of Pruning Rule for Unordered Search and its Application to Classification Learning. In K. Ramamohanarao (Ed.), Australian Computer Science Communications Vol. 18 (1): Proceedings of the Nineteenth Australasian Computer Science Conference (ACSC'96) Royal Melbourne Insitute of Technology, Australia. Melbourne: ACS, pages 1-10. [Abstract] [PDF]
2.Webb, G. I. (1995). OPUS: An Efficient Admissible Algorithm For Unordered Search. Journal of Artificial Intelligence Research 3. Menlo Park, CA: AAAI Press, pages 431-465. [Abstract] [Link to paper via JAIR website]
1.Webb, G. I. (1993). Systematic Search for Categorical Attribute-Value Data-Driven Machine Learning. In C. Rowles, H. Liu and N. Foo (Eds.), Proceedings of the Sixth Australian Joint Conference on Artificial Intelligence (AI'93) Melbourne, Australia. Singapore: World Scientific, pages 342-347. [Abstract] [Pre-publication PDF]