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    Item type:Publication,
    Incremental multilevel association rule mining of a dynamic database under a change of a minimum support threshold
    (2016-01-01)
    Pumjun, Nophadon
    ;
    The proposed algorithm can efficiently deal with multilevel association rules mining of a dynamic database and a current support threshold can be different from the previous task. The experimental results show that the proposed algorithm has better performance than ML-T2 algorithm.
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    Item type:Publication,
    Maintenance of multi-level association rules discovery in dynamic database under a change of support threshold
    (2016-01-13)
    Pumjun, Nophadon
    ;
    An association rule mining is often performed with a dynamic database and hierarchical items. The big problem of data mining process is a maintenance association rules while the database always changing. The purpose of this study is to extend the MLUp algorithm which can maintain a multilevel association rules discovery at the same minimum support threshold. In general, several mining tasks are required to deal with different support thresholds. MLUpCS can deal with a maintaining of mining multilevel association rules in dynamic databases under the different support threshold without re-mine a whole database. The result of MLUpCS algorithm experiment has shown how better performance than ML-T2 algorithm. The experimental results show the superior performance of MLUpCS when compared with ML-T2.