Abstract
In the traditional frequent itemsets mining on transactional databases, which items have no weight (equal weight, as equal to 1). However, in real world applications are often each item has a different weight (the importance/significance of each item). Therefore, we need to mining weighted/significance itemsets on transactional databases. In this paper, we propose a high-performance algorithm called HPM-FSI for mining frequent significance itemsets based on approach NOT satisfy the downward closure property (a great challenge). The experimental results show that the proposed algorithms perform better than other existing algorithms on both real-life and synthetic datasets.
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Acknowledgements
This work was supported by University of Social Sciences and Humanities; University of Science, VNU-HCMC, Ho Chi Minh City, Vietnam.
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Phan, H., Le, B. (2020). HPM-FSI: A High-Performance Algorithm for Mining Frequent Significance Itemsets. In: Liu, Y., Wang, L., Zhao, L., Yu, Z. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2019. Advances in Intelligent Systems and Computing, vol 1075. Springer, Cham. https://doi.org/10.1007/978-3-030-32591-6_3
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DOI: https://doi.org/10.1007/978-3-030-32591-6_3
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