Frequent Itemset Mining Approaches: An Analytical Review of Contemporary Methodologies

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Giriraj Bhat; Pranam R Betrabet; Shivani Adiga

Frequent Itemset Mining Approaches: An Analytical Review of Contemporary Methodologies

Authors: Giriraj Bhat, Pranam R Betrabet, Shivani Adiga

Unique Paper ID: 180375
Volume: 12
Issue: 1
PageNo: 1397-1402

Keywords: Data mining Pattern discovery Itemset enumeration Scalable algorithms Association mining Transaction analysis

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.

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

Frequent Itemset Mining Approaches: An Analytical Review of Contemporary Methodologies

Available:https://ijirt.org/article?manuscript=180375

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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