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Erschienen in:
2014 | OriginalPaper | Buchkapitel
A Novel Method for Mining Class Association Rules with Itemset Constraints
verfasst von : Dang Nguyen, Bay Vo, Bac Le
Erschienen in: Computational Collective Intelligence. Technologies and Applications
Verlag: Springer International Publishing
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Mining class association rules with itemset constraints is very popular in mining medical datasets. For example, when classifying which populations are at high risk for the HIV infection, epidemiologists often concentrate on rules which include demographic information such as sex, age, and marital status in the rule antecedents. However, two existing methods, post-processing and pre-processing, require much time and effort. In this paper, we propose a lattice-based approach for efficiently mining class association rules with itemset constraints. We first build a lattice structure to store all frequent itemsets. We then use paternity relations among nodes to discover rules satisfying the constraint without re-building the lattice. The experimental results show that our proposed method outperforms other methods in the mining time.