Parallel Branch-and-Bound Graph Search for Correlated Association Rules
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There have been proposed efficient ways of enumerating all the association rules that are interesting with respect to support, confidence, or other measures. In contrast, we examine the optimization problem of computing the optimal association rule that maximizes the significance of the correlation between the assumption and the conclusion of the rule. We propose a parallel branch-and-bound graph search algorithm tailored to this problem. The key features of the design are (1) novel branch-and-bound heuristics, and (2) a rule of rewriting conjunctions that avoids maintaining the list of visited nodes. Experiments on two different types of large-scale shared-memory multi-processors confirm that the speed-up of the computation time scales almost linearly with the number of processors, and the size of search space could be dramatically reduced by the branch-and-bound heuristics.
KeywordsExecution Time Association Rule Search Tree Mining Association Rule Apriori Algorithm
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- 1.R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases. In Proceedings of ACM SIGMOD, pages 207–216, May 1993.Google Scholar
- 2.R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In Proceedings of VLDB Conference, pages 487–499, 1994.Google Scholar
- 3.G. Y. Ananth, V. Kumar, and P. Pardalos. Parallel processing of discrete optimization problems. 1993.Google Scholar
- 5.S. Brin, R. Motwani, and C. Silverstein. Beyond market baskets: Generalizing association rules tocorrelations. In Proceedings of ACM SIGMOD, pages 265–276. SIGMOD Record 26(2), June 1997.Google Scholar
- 6.V. Kumar, A. Grama, and G. Karypis. Introduction to Parallel Computing: Design and Analysis of Algorithms. Benjamin Cummings, Nov. 1993.Google Scholar
- 7.S. Morishita. On classification and regression. In Proceedings of Discovery Science, DS’98, Lecture Notes in Artificial Intelligence, volume 1532, pages 40–57, Dec. 1998.Google Scholar
- 8.R. T. Ng, L. V. Lakshmanan, J. Han, and A. Pang. Exploratory mining and pruning optimizations of constrained association rules. In Proceedings of ACM SIGMOD, pages 13–24, June 1998.Google Scholar
- 9.J. S. Park, M.-S. Chen, and P. S. Yu. An effective hash-based algorithm for mining association rules. In Proceedings of ACM SIGMOD, pages 175–186, May 1995.Google Scholar
- 10.R. J. Bayardo Jr. Efficiently Mining Long Patterns from Databases. In Proceedings of ACM SIGMOD, pages 85–93, June 1998.Google Scholar
- 11.R. J. Bayardo Jr., R. Agrawal, D. Gunopulos. Constraint-Based Rule Mining in Large, Dense Databases. In Proceedings of ICDE, pages 188–197, March 1999.Google Scholar
- 12.R. Srikant and R. Agrawal. Mining quantitative association rules in large relational tables. In Proceedings of ACM SIGMOD, June 1996.Google Scholar