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HPM-FSI: A High-Performance Algorithm for Mining Frequent Significance Itemsets

  • Huan PhanEmail author
  • Bac Le
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1075)

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.

Keywords

Frequent significance itemsets High-performance HPM-FSI algorithm NOT satisfy the downward closure property 

Notes

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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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Faculty of Mathematics and Computer ScienceUniversity of Science, VNU-HCMCHo Chi Minh CityVietnam
  2. 2.Division of ITUniversity of Social Sciences and Humanities, VNU-HCMCHo Chi Minh CityVietnam
  3. 3.Faculty of ITUniversity of Science, VNU-HCMCHo Chi Minh CityVietnam

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