Improvement of Apriori Algorithm Performance Using the TID-List Vertical Approach and Data Partitioning

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)