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Author: search.filters.author.Thurachon, WannasiriSubject: search.filters.subject.data mining

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    Item type:Publication,
    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.