Geoffrey I. Webb
- 1.R. Agrawal, T. Imielinski, and A. Swami. Mining associations between sets of items in massive databases. In Proceedings of the 1993 ACM-SIGMOD International Conference on Management of Data, pages 207-216, 1993.]] Google Scholar
Digital Library
- 2.R. Agrawal, H. Mannila, R. Srikant, H. Toivonen, and A. I. Verkamo. Fast discovery of association rules. In U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, editors, Advances in Knowledge Discovery and Data Mining, pages 307-328. AAAI Press, Menlo Park, CA., 1996.]] Google ScholarDigital Library
- 3.R. J. Bayardo, and R. Agrawal. Mining the most interesting rules. In Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 145-154, 1999.]] Google ScholarDigital Library
- 4.R. J. Bayardo, R. Agrawal, and D. Gunopulos. Constraint-based rule mining in large, dense databases. Data Mining and Knowledge Discovery, 4(2/3):217-240, 2000.]] Google ScholarDigital Library
- 5.J. A. Blackard. Comparison of Neural Networks and Discriminant Analysis in Predicting Forest Cover Types. PhD thesis, Colorado State University Department ofForest Sciences, Fort Collins, Colorado, 1998.]] Google ScholarDigital Library
- 6.C. Borgelt. apriori. (Computer Software) http://fuzzy.cs.Uni-Magdeburg.de/ borgelt/, February 2000.]]Google Scholar
- 7.P. Clark and T. Niblett. The CN2 induction algorithm. Machine Learning, 3:261-284, 1989.]] Google ScholarDigital Library
- 8.S. H. Clearwater and F. J. Provost. RL4: A tool for knowledge-based induction. In Proceedings of Second Intl. IEEE Conf. on Tools for AI, pages 24-30, Los Alamitos, CA, 1990. IEEE Computer Society Pres.]]Google ScholarCross Ref
- 9.R. S. Michalski. A theory and methodology of inductive learning. In R. S. Michalski, J. G. Carbonell, and T. M. Mitchell, editors, Machine Learning: An Artificial Intelligence Approach, pages 83-129. Springer-Verlag, Berlin, 1983.]]Google Scholar
- 10.S. Morishita and A. Nakaya. Parallel branch-and-bound graph search for correlated association rules. In Proceedings of the ACM SIGKDD Workshop on Large-Scale Parallel KDD Systems, volume LNAI 1759, pages 127-144. Springer, Berlin, 2000.]] Google ScholarDigital Library
- 11.J. S. Park, M.-S. Chen, and S. Y. Philip. An effective hash based algorithm for mining assiociation rules. In Proceedings of the ACM SIGMOD International Conference on Management of Data, pages 175-186. ACM Press, 1995.]] Google ScholarDigital Library
- 12.F. Provost, J. Aronis, and B. Buchanan. Rule-space search for knowledge-based discovery. CIIO Working Paper IS 99-012, Stern School of Business, New York University, , NY, NY 10012, 1999.]]Google Scholar
- 13.J. R. Quinlan. Generating production rules from decision trees. In IJCAI 87: Proceedings of the Tenth International Joint Conference onArticial Intelligence, pages 304-307, Los Altos, 1987. Morgan Kaufmann.]]Google Scholar
- 14.R. Rymon. Search through systematic set enumeration. In Proceedings KR-92, pages 268-275, Cambridge, MA, 1992.]]Google Scholar
- 15.A. Savasere, E. Omiecinski, and S. Navathe. An efficient algorithm for mining association rules in large databases. In Proceedings of the 21st International Conference onVery Large Data Bases, pages 432-444. Morgan Kaufmann, 1995.]] Google ScholarDigital Library
- 16.R. Segal and O. Etzioni. Learning decision lists using homogeneous rules. In AAAI-94, Seattle, WA, 1994. AAAI press.]] Google ScholarDigital Library
- 17.H. Toivonen. Sample large databases for association rules. In Proceedings of the 22nd International Conference onVery Large Data Bases, pages 134-145. Morgan Kaufmann, 1996.]] Google ScholarDigital Library
- 18.G. I. Webb. OPUS: An efficient admissible algorithm for unordered search. Journal of Artificial Intelligence Research, 3:431-465, 1995.]]Google ScholarDigital Library
- 19.G. I. Webb. Inclusive pruning: A new class of pruning rule for unordered search and its application to classification learning. In Proceedings of the Nineteenth Australasian Computer Science Conference, pages 1-10, Melbourne, January 1996.]]Google Scholar
Index Terms
- Efficient search for association rules
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