@article{MAKHILLIJSC201611321331,
    title = {A New Approach Towards Item Set Mining Using Distribution Model},
    journal = {International Journal of Soft Computing},
    volume = {11},
    number = {3},
    pages = {155-159},
    year = {2016},
    issn = {1816-9503},
    doi = {ijscomp.2016.155.159},
    url = {https://makhillpublications.co/view-article.php?issn=1816-9503&doi=ijscomp.2016.155.159},
    author = {Paul and},
    keywords = {Data mining,Knowledge Discovery Database (KDD),itemsets,utility,frequency},
    abstract = {Data mining can be defined as an activity that extracts some new nontrivial information contained in large databases. Traditional data mining techniques have focused largely on detecting the statistical correlations between the items that are more frequent in the transaction databases. Generally, several applications are using data mining in different fields like medical, marketing and so on. Numerous methods and techniques have been developed for mining the information from the databases. In this study, we propose a new approach for itemset mining on utility and frequency using distribution model and association rule mining based research works.}
    }