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Adaptive Constraint Pushing in Frequent Pattern Mining (Articolo in rivista)

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Adaptive Constraint Pushing in Frequent Pattern Mining (Articolo in rivista) (literal)

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Bonchi F., Giannotti F., Mazzanti A., Pedreschi D., (2003)
Adaptive Constraint Pushing in Frequent Pattern Mining
in Lecture notes in computer science
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In: Knowledge Discovery in Databases: PKDD 2003 (Cavtat-Dubrovnik, Croatia, 22-26 September 2003). Proceedings, p. 12. Nada Lavrac and Dragan Gamberger and Hendrik Blockeel and Ljupco (eds.). (Lecture Notes in Computer Science, vol. 2838). Springer, 2003. (literal)

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Abstract

Pushing monotone constraints in frequent pattern mining can help pruning the search space, but at the same time it can also reduce the effectiveness of anti-monotone pruning. There is a clear tradeoff. Is it better to exploit more monotone pruning at the cost of less anti-monotone pruning, or viceversa? The answer depends on characteristics of the dataset and the selectivity of constraints. In this paper, we deeply characterize this trade-off and its related computational problem. As a result of this characterization, we introduce an adaptive strategy, named ACP (Adaptive Constraint Pushing) which exploits any conjunction of monotone and anti-monotone constraints to prune the search space, and level by level adapts the pruning to the input dataset and constraints, in order to maximize efficiency. (literal)

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