Abstract

The Apriori algorithm is one of the most widely developed and used association rule algorithms because it can produce optimal rules.The association rule a priori algorithm has three main problems: the old dataset scan process, less than optimal rules formed, and the use of large memory.Therefore, this study aims to improve the performance of the a priori algorithm in the itemset frequency search process with optimal rules, small memory usage, and fast dataset scans.The method used in this study is TID-List Vertical and data partitioning.This study result indicates that the TPQ-Apriori method improves the performance of the dataset scan process up to two times faster in scanning datasets compared to the study's results using the traditional a priori method.The performance of the dataset scan process from the results of this study is also superior in processing speed by up to 7% on scanning datasets compared to the ETDPC-Apriori method on testing three data sets (mushroom, chess, and c20d10k).

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