In this paper, we propose a new optimization approach to the APRIORI reference algorithm (AGR 94) for 2-itemsets (sets of cardinal 2). The approach used is based on two-item sets. We start by calculating the 1-itemets supports (cardinal 1 sets), then we prune the 1-itemsets that are not frequent and keep only those that are frequent (ie those that have the itemsets whose values are greater than or equal to a fixed minimum threshold). During the second iteration, we sort the frequent 1-itemsets in descending order of their respective supports and then we form the 2-itemsets. In this way the rules of association are discovered more quickly. Experimentally, the comparison of our YELLI algorithm with APRIORI, PASCAL, CLOSE and MAX-MINER shows its efficiency on weakly correlated data. Our work has also led to a classical model of side-by-side classification of items that we have obtained by establishing a relationship between the different sets of 2-itemsets.

Keywords: : Optimization, Frequent Itemsets, Association Rules, Low-Correlation Data, Supports