A Patricia-Tree Approach For Frequent Closed Itemsets
Moez Ben Hadj Hamida, SlimaniI, Yahya · Zenodo (CERN European Organization for Nuclear Research) · 2007
In this paper, we propose an adaptation of the Patricia-Tree for sparse datasets to generate non redundant rule associations. Using this adaptation, we can generate frequent closed itemsets that are more compact than frequent itemsets used in Apriori approach. This adaptation has been experimented on a set of datasets benchmarks.