par Tatti, Nikolaj;Moerchen, Fabian;Calders, Toon 
Référence ACM transactions on database systems, 39, 3, page (20)
Publication Publié, 2014
Référence ACM transactions on database systems, 39, 3, page (20)
Publication Publié, 2014
Article révisé par les pairs
| Résumé : | Mining frequent patterns is plagued by the problem of pattern explosion, making pattern reduction techniques a key challenge in pattern mining. In this article we propose a novel theoretical framework for pattern reduction by measuring the robustness of a property of an itemset such as closedness or nonderivability. The robustness of a property is the probability that this property holds on random subsets of the original data. We study four properties, namely an itemset being closed, free, non-derivable, or totally shattered, and demonstrate how to compute the robustness analytically without actually sampling the data. Our concept of robustness has many advantages: Unlike statistical approaches for reducing patterns, we do not assume a null hypothesis or any noise model and, in contrast to noise-tolerant or approximate patterns, the robust patterns for a given property are always a subset of the patterns with this property. If the underlying property is monotonie then the measure is also monotonie, allowing us to efficiently mine robust itemsets. We further derive a parameter-free technique for ranking itemsets that can be used for top-k approaches. Our experiments demonstrate that we can successfully use the robustness measure to reduce the number of patterns and that ranking yields interesting itemsets. |
