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Author: search.filters.author.Kreesuradej, WorapojSubject: search.filters.subject.data mining

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    Incremental Association Rule Mining with a Fast Incremental Updating Frequent Pattern Growth Algorithm
    (2021-01-01)
    Thurachon, Wannasiri
    ;
    Kreesuradej, Worapoj
    One of the most challenging tasks in association rule mining is that when a new incremental database is added to an original database, some existing frequent itemsets may become infrequent itemsets and vice versa. As a result, some previous association rules may become invalid and some new association rules may emerge. We designed a new, more efficient approach for incremental associationrule mining using a Fast Incremental Updating Frequent Pattern growth algorithm (FIUFP-Growth), a new Incremental Conditional Pattern tree (ICP-tree), and a compact sub-tree suitable for incrementalmining of frequent itemsets. This algorithm retrieves previous frequent itemsets that have already been mined from the original database and their support counts then use them to efficiently mine frequent itemsets from the updated database and ICP-tree, reducing the number of rescans of the original database. Our algorithm reduced usages of resource and time for unnecessary sub-tree construction compared to individual FP- Growth, FUFP-tree maintenance, Pre-FUFP, and FCFPIM algorithms. From the results, at 3% minimum support threshold, the average execution time for pattern growth mining of our algorithm performs 46% faster than FP- Growth, FUFP-tree, Pre-FUFP, and FCFPIM. This approach to incremental association rule mining and our experimental findings may directly benefit designers and developers of computer business intelligence methods.
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    A Technique for Estimating Updated Frequent Itemsets in ESC-Growth Algorithm
    (2019-07-01)
    Kreesuradej, Worapoj
    ;
    Thurachon, Wannasiri
    In discovering association rules from a dynamic database, iteration through the frequent itemsets requires significant resources and computational time for construction of sub-trees, sub-tree traversal and generation of conditional pattern bases, and it is quite possible that no updated frequent itemsets may have been found at all, resulting in a waste of resources and computational time. We describe a technique for estimating the support count for the itemsets for the next iteration of discovery of the frequent itemsets by our ESC-Growth Algorithm. This technique reduces the need to construct a new sub-tree and next discovery step. If no frequent itemsets in the updated database have been found in the next iteration, ESC-Growth will not construct a new sub-tree and will stop discovering new frequent itemsets in that iteration, reducing the waste of resources and computational time. We measured execution time and sub-tree counts for FP-Growth, FUFP-tree, FPISC-Growth and ESCGrowth on the same synthetic dataset; we found that, at 5% minimum support threshold, ESC-Growth used only 40.3, 96.5, and 99.6% of the execution time required by FP-Growth, FUFPtree and FPISC-Growth, respectively.
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    Frequent itemsets mining using random walks for record insertion and deletion
    (2017-02-23)
    Thusaranon, Panita
    ;
    Kreesuradej, Worapoj
    In Association rules mining, the task of finding frequent itemsets in dynamic database is very important because the updates may not only invalidate some existing rules but also make other rules relevant. In this paper, we propose a new algorithm to maintain frequent itemsets of a dynamic database in the case of record insertion as well as deletion simultaneously. Basically, the proposed algorithm maintains not only the support counts of frequent itemsets but also the support counts of prospective frequent itemsets, i.e., infrequent itemsets that promise to be frequent in the future, in an original database. Prospective frequent itemsets, which are obtained by using the principle of Random Walks, can help to reduce a number of times to rescan the original database.
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    Maintenance of multi-level association rules discovery in dynamic database under a change of support threshold
    (2016-01-13)
    Pumjun, Nophadon
    ;
    Kreesuradej, Worapoj
    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.