Kreesuradej, Worapoj

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Kreesuradej, Worapoj
Alternative Name
Kreesuradej, W.
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Email
worapoj.kr@kmitl.ac.th

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    Item type:Publication,
    An enhanced incremental association rule discovery with a lower minimum support
    (2016-12-01)
    Ariya, Araya
    ;
    Kreesuradej, Worapoj
    In the real world of data, a new set of data has been being inserted into the existing database. Thus, the rule maintenance of association rule discovery in large databases is an important problem. Every time the new data set is appended to an original database, the old rule may probably be valid or invalid. This paper proposed the approach to calculate the lower minimum support for collecting the expected frequent itemsets. The concept idea is applying the normal approximation to the binomial theory. This proposed idea can reduce a process of calculating probability value for all itemsets that are unnecessary. In addition, the confidence interval is also applied to ensure that the collection of expected frequent itemsets is properly kept.
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    Item type:Publication,
    Probability-based incremental association rule discovery using the normal approximation
    (2013-01-01)
    Ariya, Araya
    ;
    Kreesuradej, Worapoj
    An incremental association rules mining is one of an association rule mining research topics which finds the relation between set of item in dynamic databases. As data grows up rapidly, the co-occurrence itemset which discovered in the previous mining may be changed and the association rule will be change consequently. Incremental association rule mining research attempts to maintain that rules. Probability-based algorithm, one of an incremental algorithm, applied the principle of Bernoulli trial to predict expected frequent itemsets for reducing collected border itemsets and a number of times to rescan the original database. However, the numerical problem will occur when the algorithm deals with a large database. To manipulate with this problem, the improved probability-based incremental association rule discovery using normal approximation to estimate the probability of occurrence of expected frequent itemset is introduced in this paper. In addition, the confidence interval is applied to ensure that the collecting of expected frequent itemsets is properly kept. © 2013 IEEE.