@article{MAKHILLJEAS201813715953,
    title = {Usage of Dimension Tree and Modified FP-Growth Algorithm for
Association Rule Mining on Large Volumes of Data},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {13},
    number = {7},
    pages = {1670-1675},
    year = {2018},
    issn = {1816-949x},
    doi = {jeasci.2018.1670.1675},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2018.1670.1675},
    author = {V. and},
    keywords = {Association rule mining,ARM,FP-tree,frequent itemset mining,scans,huge,methods},
    abstract = {Performing association rule mining on huge volume of data is the dominant area of research.
Identifying the interesting correlations among different data item is a beneficial task for correct and appropriate
decision making. During association rule mining process, finding frequent itemset is the key area as it needs
many number of scans over database and huge memory. Among several methods, FP growth needs only one
scan over the database. But it generates huge number of intermediate candidate itemsets. Hence, in this study,
we present a novel algorithm of association rule mining which is a modified version of FP-growth method using
dimension tree. Experimental results show that the proposed method yields good results compared to traditional
methods and generates less number of intermediate candidate itemsets.}
    }