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September 12, 2025

An apriori algorithm-based framework for measuring and improving transactional dataset efficiency

Key Points

The enhancement reduces execution time by 67.38% for transactional datasets, making data mining more efficient.
Experiments demonstrated that focusing on relevant dealings avoids unnecessary database scans, improving performance.
The improved version of the apriori algorithm addresses efficiency issues found in traditional methods for large datasets.
This approach significantly advances knowledge discovery in data mining by streamlining the search for frequent product sets.

Abstract

There are plenty of algorithms designed for mining association rules, but Apriori stands out as one of the most popular. It’s especially useful for identifying frequent product sets in large datasets and uncovering association rules that help with knowledge discovery. However, the innovative Apriori algorithm has a significant drawback: it spends too much time scanning the entire database repeatedly to find frequent product sets. This paper tackles that issue by presenting an enhanced Apriori algorithm version. The enhanced approach avoids the need for multiple full-database scans by focusing only on a subset of relevant dealings. Experiments conducted on various transaction groups and with different tiniest support values reveal that the amended algorithm slashes execution time by an impressive 67.38% compared to the innovative. These results clearly show how the proposed enhancement makes Apriori much more efficient and time-saving, offering a practical solution for modern data mining challenges.

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