ExAMiner: optimized level-wise frequent pattern mining with monotone constraints
- 23 April 2004
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
Abstract
The key point is that, in frequent pattern mining, the most appropriate way of exploiting monotone constraints in conjunction with frequency is to use them in order to reduce the problem input together with the search space. Following this intuition, we introduce ExAMiner, a level-wise algorithm which exploits the real synergy of antimonotone and monotone constraints: the total benefit is greater than the sum of the two individual benefits. ExAMiner generalizes the basic idea of the preprocessing algorithm ExAnte [F. Bonchi et al., (2003)], embedding such ideas at all levels of an Apriori-like computation. The resulting algorithm is the generalization of the Apriori algorithm when a conjunction of monotone constraints is conjoined to the frequency antimonotone constraint. Experimental results confirm that this is, so far, the most efficient way of attacking the computational problem in analysis.Keywords
This publication has 9 references indexed in Scilit:
- Adaptive and resource-aware mining of frequent setsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- ExAnte: Anticipated Data Reduction in Constrained Pattern MiningPublished by Springer Nature ,2003
- Adaptive Constraint Pushing in Frequent Pattern MiningPublished by Springer Nature ,2003
- Optimization of association rule mining queriesIntelligent Data Analysis, 2002
- DualMinerPublished by Association for Computing Machinery (ACM) ,2002
- Real world performance of association rule algorithmsPublished by Association for Computing Machinery (ACM) ,2001
- Constraint-based, multidimensional data miningComputer, 1999
- Exploratory mining and pruning optimizations of constrained associations rulesPublished by Association for Computing Machinery (ACM) ,1998
- An effective hash-based algorithm for mining association rulesACM SIGMOD Record, 1995