Association Rule Mining Bibliography

Journal articles

1. I.N.M. Shaharanee, F. Hadzic and T.S. Dillon. Interestingness measures for association rules based on statistical validity. Knowledge Based Systems, Volume 24, Issue 3 (2011), Page 386-392.
2. L. Feng, T.S. Dillon and J. Liu. Inter-transactional association rules for multi-dimensional contexts for prediction and their application to studying meteorological data. Data & Knowledge Engineering, Volume 37, Issue 1 (2001), Page 85-115.

Conference contributions

1. I.N.M. Shaharanee, F. Hadzic and T.S. Dillon. A Statistical Interestingness Measures for XML Based Association Rules, Proceedings of the 11th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2010, Daegu, Korea, pag: 194-205, ISBN: 978-3-642-15245-0.
2. I.N.M. Shaharanee, F. Hadzic and T.S. Dillon. Interestingness of Association Rules Using Symmetrical Tau and Logistic Regression, Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence, AI 2009, Melbourne, Australia, pag: 422-431, ISBN: 978-3-642-10438-1.
3. L. Feng and T.S. Dillon. Mining Interesting XML-Enabled Association Rules with Templates, Proceedings of the 3th International Workshop on Knowledge Discovery inInductive Databases, KDID 2004, Pisa, Italy, pag: 66-88, ISBN: 3-540-25082-4.
4. L. Feng, T.S. Dillon, H. Weigand and E. Chang. An XML-Enabled Association Rule Framework, Proceedings of the 14th International Conference on Database and Expert Systems Applications, DEXA 2003, Prague, Czech Republic, pag: 88-97, ISBN: 3-540-40806-1.