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A sparse memory allocation data structure for sequential and parallel association rule mining

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Abstract

In this paper, we present a sparse memory allocation data structure for sequential and parallel data mining. We explored three algorithms utilizing the proposed data structure: MASP-tree, apriori-TID, and FP-growth. We modified the data structure of apriori-TID and FP-growth algorithms to reduce memory allocation cost. Five data sets are used for comparison. The results show that the modified apriori-TID has a higher speed-up than the modified FP-growth when the proposed data structure is used. A maximum speed-up of 3.42 is observed when MASP algorithm is tested.

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Acknowledgments

The authors would like to thank LA DOTD for continuous support in research.

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Authors and Affiliations

  1. Highway Safety Research Group, Louisiana State University, 3535 Nicholson Ext., Baton Rouge, LA, 70803, USA

    Ömer M. Soysal, Eera Gupta & Harisha Donepudi

Authors
  1. Ömer M. Soysal
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  2. Eera Gupta
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  3. Harisha Donepudi
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Corresponding author

Correspondence to Ömer M. Soysal.

Appendix

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See Table 3.

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Soysal, Ö.M., Gupta, E. & Donepudi, H. A sparse memory allocation data structure for sequential and parallel association rule mining. J Supercomput 72, 347–370 (2016). https://doi.org/10.1007/s11227-015-1566-x

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