Frequent Itemset Mining Approaches: An Analytical Review of Contemporary Methodologies

  • Unique Paper ID: 180375
  • Volume: 12
  • Issue: 1
  • PageNo: 1397-1402
  • Abstract:
  • The extraction of frequent itemsets is one of a fundamental techniques in data mining which deals with the discovery of combinations of items that appear together frequently in transactional data. This review summarizes the history and contemporary approaches of frequent itemset mining, including their algorithms and innovations. We explore the shift from traditional breadth-first techniques to modern parallel, distributed, and optimized methods for large data set processing. This review presents the results of eight studies that show significant improvements in efficiency, memory usage, and applicability to real world problems. The study's findings indicate new areas of research for accelerating computations with GPUs, mining with privacy considerations, and working with streams of data, while addressing enduring issues and suggesting new directions for research.

Copyright & License

Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

BibTeX

@article{180375,
        author = {Giriraj Bhat and Pranam R Betrabet and Shivani Adiga},
        title = {Frequent Itemset Mining Approaches: An Analytical Review of Contemporary Methodologies},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {1397-1402},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180375},
        abstract = {The extraction of frequent itemsets is one of 
a fundamental techniques in data mining which deals 
with the discovery of combinations of items that appear 
together frequently in transactional data. This review 
summarizes the history and contemporary approaches 
of frequent itemset mining, including their algorithms 
and innovations. We explore the shift from traditional 
breadth-first 
techniques 
to 
modern parallel, 
distributed, and optimized methods for large data set 
processing. This review presents the results of eight 
studies that show significant improvements in 
efficiency, memory usage, and applicability to real
world problems. The study's findings indicate new 
areas of research for accelerating computations with 
GPUs, mining with privacy considerations, and 
working with streams of data, while addressing 
enduring issues and suggesting new directions for 
research.},
        keywords = {Data mining, Pattern discovery, Itemset  enumeration, Scalable algorithms, Association mining,  Transaction analysis},
        month = {June},
        }

Cite This Article

  • ISSN: 2349-6002
  • Volume: 12
  • Issue: 1
  • PageNo: 1397-1402

Frequent Itemset Mining Approaches: An Analytical Review of Contemporary Methodologies

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