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.
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Bonchi, F., Giannotti, F., Mazzanti, A., Pedreschi, D. (2003). Adaptive Constraint Pushing in Frequent Pattern Mining. In: Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds) Knowledge Discovery in Databases: PKDD 2003. PKDD 2003. Lecture Notes in Computer Science(), vol 2838. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39804-2_7
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DOI: https://doi.org/10.1007/978-3-540-39804-2_7
Publisher Name: Springer, Berlin, Heidelberg
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