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@article{147526,
        author = {Utkarsh Kumar Shrivastava},
        title = {AN EMPIRICAL STUDY OF VARIOUS FREQUENT ITEM SET MINING TECHNIQUES},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {5},
        number = {8},
        pages = {249-252},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=147526},
        abstract = {Frequent item set mining has been a heart favorite theme for data mining researchers for over a decade. A large amount of literature has been dedicated to this research and tremendous progress has been made, ranging from efficient and scalable algorithms for frequent itemset mining in transaction databases to numerous research frontiers, such as sequential pattern mining, structured pattern mining, correlation mining, associative classification, and frequent pattern-based clustering, as well as their broad applications. In this paper, a literature review of various latest techniques for mining frequent items from a transaction data base are presented in critical manner, },
        keywords = {Data Mining, Frequent Pattern Mining, Support, Confidence, Apriori, DIC, Partitioning.},
        month = {},
        }