There are several data mining methods for categorization using association rules nowadays, the most famous of which being the Apriori algorithm. By searching the whole database for k-element frequent item sets, the Apriori method is used to define frequent itemsets from large transactional data sets. According to the Apriori algorithm, we are going to re-evaluate and re-evaluate access time which consumes in scanning the database for k-times looking for k-element frequent item set. In this paper, we will analyse and compare our proposed Updated Hybrid-Apriori Algorithm (HuApriori) with the original Apriori algorithm, which concludes the experimental result to calculate frequent items on several groups of transactions with minimal support (for both Apriori and HuApriori) and improves its performance by reducing the time spent accessing the database by 55%. Our proposed HuApriori algorithm is an enhanced version of Apriori algoritam[4] and working greatly better at each parameter which we include in concluding the results.