A New Extraction Optimization Approach to Frequent 2 Item sets

International Journal on Computational Science Applications (IJCSA) ISSN : 2200 – 0011 https://wireilla.com/ijcsa/index.html Current Issue Article Title: A New Extraction Optimization Approach to Frequent 2 Item sets Abstract 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 not frequent and keep only those that are frequent (ie those with the item sets 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 algorithm OPTI2I with APRIORI, PASCAL, CLOSE and MAXMINER, shows its efficiency on weakly correlated data. Our work has also led to a classical model of sideby-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 For More Details: https://wireilla.com/papers/ijcsa/V9N2/9219ijcsa01.pd