Abstract
In recent years, data mining has been widely used to extract information from big data. Frequent-itemset mining is one of the most popular techniques in data mining. Erasable-itemset mining is used for production planning and is one of the various itemset-mining problems. Factories could apply erasable-itemset mining to discover suitable material combinations that are erasable but do not greatly influence the profits. However, as time goes, new products may be entered into the factories. We have previously proposed a unified general algorithm for considering different scenarios of itemset lifetimes. Users can decide their desired scenario according to their requirements. The unified algorithm, however, suffers from its execution time because it needs to consider all the different situations. In this work, we propose a dedicated algorithm for handling a special scenario faster than the unified algorithm. We consider the influence on the profits of the itemsets of products first entering and discuss the influence on the lifespans of different itemsets. Experiments were conducted to compare the performance of the dedicated and the unified proposed approaches under different parameter settings.
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This work was supported by the grant MOST 109-2221-E-390-015-MY3, the Ministry of Science and Technology, Taiwan.
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Hong, TP., Chang, H., Li, SM., Tsai, YC. (2022). A Dedicated Temporal Erasable-Itemset Mining Algorithm. In: Abraham, A., Gandhi, N., Hanne, T., Hong, TP., Nogueira Rios, T., Ding, W. (eds) Intelligent Systems Design and Applications. ISDA 2021. Lecture Notes in Networks and Systems, vol 418. Springer, Cham. https://doi.org/10.1007/978-3-030-96308-8_91
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DOI: https://doi.org/10.1007/978-3-030-96308-8_91
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