A Heuristic Approach To Redefine FIS By Matrix Implementation Through Update Apriori ‘HuApriori’ In Textual Data Set (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
There are several data mining methods for categorization using association rulesnowadays, the most famous of which being the Apriori algorithm. By searching the wholedatabase for k-element frequent item sets, the Apriori method is used to define frequent itemsetsfrom large transactional data sets. According to the Apriori algorithm, we are going to reevaluateand re-evaluate access time which consumes in scanning the database for k-timeslooking for k-element frequent item set. In this paper, we will analyse and compare our proposedUpdated Hybrid-Apriori Algorithm ( HuApriori) with the original Apriori algorithm, whichconcludes the experimental result to calculate frequent items on several groups of transactionswith minimal support (for both Apriori and HuApriori) and improves its performance byreducing the time spent accessing the database by 55%. Our proposed HuApriori algorithm is anenhanced version of Apriori algoritam[4] and working greatly better at each parameter which weinclude in concluding the results.