A Heuristic Approach To Redefine FIS By Matrix Implementation Through Update Apriori ‘HuApriori’ In Textual Data Set
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Abstract
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 reevaluate
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.
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