A Model of Mining Noise-Tolerant Frequent Itemset in Transactional Databases

Nowadays, mining approximate frequent itemsets from noisy data has attracted much attention in real applications. However, there is not widely accepted algorithm at present to solve the problem under noisy databases, which dues to two key issues. Firstly, the anti-monotonicity property does not hold which is used to prune candidate itemsets efficiently. And secondly, the computation of support counting turns out to be NP-hard. In this paper, we propose a novel model which is based on rough set theory and capable to recover the noise-tolerant frequent itemsets from "reduced itemsets". The novel model applies depth-first growing method to generate candidate itemsets and exerts effective pruning strategies, which narrows the searching space and mines indeed meaningful noise-tolerant frequent itemsets efficiently.