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Author: search.filters.author.Kreesuradej, WorapojSubject: search.filters.subject.Incremental association rule discovery

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    Discovery of incremental association rules based on a new FP-growth algorithm
    (2019-02-01)
    Kreesuradej, Worapoj
    ;
    Thurachon, Wannasiri
    In this paper, we propose a new FP-Growth algorithm for incremental association rule discovery. We also design a new FPISC-tree based on the FUFP-tree structure. The new FPISC-tree is more suitable for the task of incremental association rule discovery than FUFP-tree structure. The basic ideas of the proposed algorithm are to retrieve the frequent itemsets from the original database and to use their support count in the update of the new support count of the incremental database so that the original paths do not need to be reprocessed as well as to strategically use them to discover frequent itemsets from the FPISC-tree. Experimental results show that the proposed algorithm was able to reduce the number of constructed subtrees and the execution time was significantly less than those of the FP-Growth and FUFP-tree.
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    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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    A probability‑based incremental association rule discovery algorithm for record insertion and deletion
    (2015-07-01)
    Thusaranon, Panita
    ;
    Kreesuradej, Worapoj
    The maintenance of association rules for dynamic database is an important problem because the updates may not only invalidate some existing rules but also make other rules relevant. This paper is the extension work of probability-based incremental association rule discovery algorithm which can only handle new data insert into a dynamic database. Unlike the previous work, the proposed algorithm can efficiently handle in case of insertion as well as deletion simultaneously. Basically, the proposed algorithm maintains the support counts of frequent itemsets and promising frequent itemsets, i.e., infrequent itemsets that promise to be frequent in the future, in an original database. Promising frequent itemsets, which are obtained by using the principle of Bernoulli trials, can help to reduce a number of times to rescan the original database. The support counts of new candidate itemsets are approximated by using the principle of maximum possible value. The experimental results show that the execution time of the proposed algorithm is faster than that of Apriori, FUP2, EDUA, and pre-large algorithm.
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    Mining dynamic databases using probability-based incremental association rule discovery algorithm
    (2009-11-20)
    Amornchewin, Ratchadaporn
    ;
    Kreesuradej, Worapoj
    In dynamic databases, new transactions are appended as time advances. This paper is concerned with applying an incremental association rule mining to extract interesting information from a dynamic database. An incremental association rule discovery can create an intelligent environment such that new information or knowledge such as changing customer preferences or new seasonal trends can be discovered in a dynamic environment. In this paper, probability-based incremental association rule discovery algorithm is proposed to deal with this problem. The proposed algorithm uses the principle of Bernoulli trials to find expected frequent itemsets. This can reduce a number of times to scan an original database. This paper also proposes a new updating and pruning algorithm that guarantee to find all frequent itemsets of an updated database efficiently. The simulation results show that the proposed algorithm has better performance than that of previous work. © J.UCS.