An Improved Apriori Algorithm for Association Rules

TL;DR

This paper introduces an improved Apriori algorithm that reduces database scanning time by selectively scanning transactions, significantly enhancing efficiency in mining association rules from large datasets.

Contribution

The paper proposes a novel modification to the Apriori algorithm that decreases scanning time by focusing on fewer transactions, improving overall efficiency.

Findings

Reduced scanning time by 67.38% compared to original Apriori

Experimental results confirm increased efficiency across various datasets

Improved algorithm maintains accuracy of association rule mining

Abstract

There are several mining algorithms of association rules. One of the most popular algorithms is Apriori that is used to extract frequent itemsets from large database and getting the association rule for discovering the knowledge. Based on this algorithm, this paper indicates the limitation of the original Apriori algorithm of wasting time for scanning the whole database searching on the frequent itemsets, and presents an improvement on Apriori by reducing that wasted time depending on scanning only some transactions. The paper shows by experimental results with several groups of transactions, and with several values of minimum support that applied on the original Apriori and our implemented improved Apriori that our improved Apriori reduces the time consumed by 67.38% in comparison with the original Apriori, and makes the Apriori algorithm more efficient and less time consuming.