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Third IEEE International Conference on Data Mining (ICDM'03)
ExAMiner: Optimized Level-wise Frequent Pattern Mining with Monotone Constraints
Melbourne, Florida
November 19-November 22
ISBN: 0-7695-1978-4
Francesco Bonchi, ISTI - C.N.R. Area della Ricerca di Pisa, Italy
Fosca Giannotti, ISTI - C.N.R. Area della Ricerca di Pisa, Italy
Alessio Mazzanti, Universit? di Pisa, Italy
Dino Pedreschi, Universit? di Pisa, Italy
The key point of this paper 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 anti-monotone 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, 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 anti-monotone constraint. Experimental results confirm that this is, so far, the most efficient way of attacking the computational problem in analysis.
Citation:
Francesco Bonchi, Fosca Giannotti, Alessio Mazzanti, Dino Pedreschi, "ExAMiner: Optimized Level-wise Frequent Pattern Mining with Monotone Constraints," icdm, pp.11, Third IEEE International Conference on Data Mining (ICDM'03), 2003
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