Frequent Itemset Mining become so popular in extracting hidden patterns from transactional databases. Among the several approaches, Apriori algorithm is known to be a basic approach which follows candidate generate and test based strategy. Although it is efficient level-wise approach, it has two limitations, (i) several passes are required to check the support of candidate itemsets. (ii) Towards more candidate itemsets and minimum threshold variations. A novel approach is proposed to tackle the above limitations. The proposed approach is one pass Hash-based Frequent Itemset Mining to derive frequent patterns. HFIM has feature that maintains candidate itemsets dynamically which are independent on minimum threshold. This feature allows to limit the number of scans over the database to one. In this paper, HFIM is compared with the Apriori to show the performance on standard datasets. The result section shows that HFIM outperforms Apriori over large databases.