Support estimation in frequent itemsets mining on Enriched Two Level Tree

Efficiently counting the support of candidate itemsets is a crucial aspect of extracting frequent itemsets because it directly impacts the overall performance of the mining process. Researchers have developed various techniques and data structures to overcome this challenge, but the problem is still open. In this paper, we investigate the two-level tree enrichment technique as a potential solution without adding significant computational overhead. In addition, we introduce ETL_Miner, a novel algorithm that provides an estimated bound for the support value of all candidate itemsets within the search space. The method presented in this article is flexible and can be used with various algorithms. To demonstrate this point, we introduce a modified version of Apriori that integrates ETL_Miner as an extra pruning phase. Preliminary empirical experimental results on both real and synthetic datasets confirm the accuracy of the proposed method and reduce the total extraction time.