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@article{156966,
        author = {Md. Ghouse Mohiuddin and Dr.L.Srinivasa Reddy},
        title = {A Novel Technique for Temporal Frequent Itemset Mining},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {5},
        pages = {425-435},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=156966},
        abstract = {Pattern mining is a powerful tool for analyzing big datasets and extracting interesting patterns from the dataset. Temporal datasets include time as an additional parameter as time-stamps that affects the data mining results. Traditional data mining techniques of finding frequent itemsets consider the static data sets and the instigated rules are relevant across the whole dataset. However, this is not the case in temporal data because, in temporal data, there are certain itemsets that are frequent over a specific period of time but would not be extracted by traditional data mining methods since their support is very low over the whole dataset. Our aim is to extract such patterns with their time intervals. In this paper, we propose a method that is able to extract different types of patterns that may exist in the temporal dataset and it is not needed by the user to specify the time periods in advance. Here we consider the time stamps as hierarchical data structures and our algorithm extracts the periodic patterns along with the time intervals.},
        keywords = {Frequent Itemset, Local Temporal Frequent Itemset, Set Superimposition, Time-Cube, Basic Time-Cube.},
        month = {},
        }