A Narrative Review of Data Mining Techniques for User Behaviour Recognition with Illustrative Application of the Apriori Algorithm

Authors

  • Sonam Department of Computer Science Engineering, Baba Mastnath University, Rohtak, Haryana, India 124001
  • Jyoti Department of Computer Science Engineering, Baba Mastnath University, Rohtak, Haryana, India 124001

DOI:

https://doi.org/10.57159/jcmm.4.2.25214

Keywords:

Data Mining, User Behavior, Apriori, FP-Growth, ECLAT, Association Rule Mining

Abstract

This review examines key data mining algorithms used for user behaviour recognition in computational systems, focusing on frequent pattern mining techniques. We summarize foundational methods such as Apriori, FP-Growth, and ECLAT, comparing their operational principles and limitations. A frequency-based literature analysis shows the widespread use of Apriori in market basket analysis. To illustrate its workings, we include a demonstrative walkthrough of the Apriori algorithm using a hypothetical dataset. The article concludes with insights into performance trade-offs and future directions in algorithmic efficiency.

References

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Published

30-04-2025

How to Cite

Sonam, & Jyoti. (2025). A Narrative Review of Data Mining Techniques for User Behaviour Recognition with Illustrative Application of the Apriori Algorithm. Journal of Computers, Mechanical and Management, 4(2), 17–23. https://doi.org/10.57159/jcmm.4.2.25214

Issue

Vol. 4 No. 2 (2025)

Section

Mini Reviews

Categories

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