Mining and Validating Localized Frequent Itemsets with Dynamic Tolerance
Olfa Nasraoui, Suchandra Goswami · 2006
We cast the frequent itemset mining problem as a criterion guided optimization problem instead of one based on exact counting. This opens several interesting possibilities, including modification of the criterion function to take into account (i) error tolerance, (ii) locality, (iii) unsupervised estimation of the error tolerance, and (iv) search strategy. We also propose a new validation procedure that takes into account the completeness and accuracy of the discovered patterns. Experiments with real Web transaction data are presented.