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Journal of Statistics and Management Systems cover

Journal of Statistics and Management Systems

(Since 1998)
Open Access ·Peer-reviewed·ISSN (Online): 2169-0014·ISSN (Print): 0972-0510
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The Journal of Statistics and Management Systems (JSMS) is a world leading journal publishing high quality, rigorously peer-reviewed original research on theoretical and applied statistics and management systems since 1998. The scope is intentionally broad, but papers must make a novel contribution to the field to be considered for publication. Topics include, but are not limited to, the following: • Statistics • Applied Statistics • Industrial Statistics • Statistical Inference • Interdisciplinary role of Statistics • Actuarial Sciences • Decision Sciences • Managerial Aspects • Management Sciences • Management Information Systems

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Journal of Statistics and Management Systems
Open Access Research Article

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

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pp. 1179–1189Vol. 28Issue 6September 2025DOI: 10.47974/JSMS-1483XML
Received:
05 Nov 2024
Published Online:
09 Sep 2025
Article type:
Research Article
Language:
EN
Article no.:
JSMS-1483
Pages:
1179–1189

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.

Keywords

Enhanced aprioriFrequent product-setsSupportCandidate product-setsComputational time

Subject Classifications

68N30

References

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