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EIFDD: An efficient approach for erasable itemset mining of very dense datasets

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Abstract

Erasable itemset mining, first proposed in 2009, is an interesting problem in supply chain optimization. The dPidset structure, a very effective structure for mining erasable itemsets, was introduced in 2014. The dPidset structure outperforms previous structures such as PID_List and NC_Set. Algorithms based on dPidset can effectively mine erasable itemsets. However, for very dense datasets, the mining time and memory usage are large. Therefore, this paper proposes an effective approach that uses the subsume concept for mining erasable itemsets for very dense datasets. The subsume concept is used to help early determine the information of a large number of erasable itemsets without the usual computational cost. Then, the erasable itemsets for very dense datasets (EIFDD) algorithm, which uses the subsume concept and the dPidset structure for the erasable itemset mining of very dense datasets, is proposed. An illustrative example is given to demonstrate the proposed algorithm. Finally, an experiment is conducted to show the effectiveness of EIFDD.

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Notes

  1. Downloaded from http://fimi.cs.helsinki.fi/data/

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Acknowledgments

This research is funded by Foundation for Science and Technology Development of Ton Duc Thang University (FOSTECT), website: http://fostect.tdt.edu.vn, under Grant FOSTECT. 2015.BR.01.

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Authors and Affiliations

  1. Faculty of Information Technology, Ho Chi Minh City University of Technology, Ho Chi Minh City, Vietnam

    Giang Nguyen

  2. Division of Data Science, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Tuong Le & Bay Vo

  3. Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Tuong Le & Bay Vo

  4. Faculty of Information Technology, University of Science, VNU, Ho Chi Minh City, Vietnam

    Bac Le

Authors
  1. Giang Nguyen
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  2. Tuong Le
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  3. Bay Vo
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  4. Bac Le
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Correspondence to Tuong Le.

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Nguyen, G., Le, T., Vo, B. et al. EIFDD: An efficient approach for erasable itemset mining of very dense datasets. Appl Intell 43, 85–94 (2015). https://doi.org/10.1007/s10489-014-0644-8

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  • DOI: https://doi.org/10.1007/s10489-014-0644-8

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