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@article{148129,
        author = {Piyush Kumar Solanki and Prof. Moksha Thakur and Prof. Amit Sariya},
        title = {A List Intersection Based Technique for mining frequent item sets from a voluminous data set},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {5},
        number = {12},
        pages = {443-447},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=148129},
        abstract = {Visit thing set mining has been a heart most cherished subject for data burrowing experts for more than 10 years. A ton of composing has been focused on this investigation and gigantic progression has been made, going from profitable and adaptable figurings for unending itemset mining in return databases to different research unsettled areas, for instance, back to back model mining, sorted out precedent mining, relationship mining, familiar request, and consistent model based clustering, similarly as their sweeping applications. In this paper, a composition review of various latest methods for mining customary things from a trade data base are presented in fundamental manner. This paper also proposes a list intersection based technique for mining all frequent item sets from a transaction data set. },
        keywords = {Data Mining, Frequent Pattern Mining, Support, Confidence, Apriori },
        month = {},
        }