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@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},
        }