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A fast algorithm for mining association rules

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Abstract

In this paper, the problem of discovering association rules between items in a lange database of sales transactions is discussed, and a novel algorithm, BitMatrix, is proposed. The proposed algorithm is fundamentally different from the known algorithms Apriori and AprioriTid. Empirical evaluation shows that the algorithm outperforms the known ones for large databases. Scale-up experiments show that the algorithm scales linearly with the number of transactions.

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References

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Authors and Affiliations

  1. National High Performance Computing Center, Department of Computer Science, University of Science & Technology of China, 230027, Hefei, P.R. China

    Huang Liusheng, Chen Huaping, Wang Xun & Chen Guoliang

Authors
  1. Huang Liusheng
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  2. Chen Huaping
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  3. Wang Xun
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  4. Chen Guoliang
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Corresponding author

Correspondence to Huang Liusheng.

Additional information

This work was supported in part by the National ‘863’ High-Tech Programme of China (No. 863-306-ZD06-2).

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Cite this article

Huang, L., Chen, H., Wang, X. et al. A fast algorithm for mining association rules. J. Comput. Sci. & Technol. 15, 619–624 (2000). https://doi.org/10.1007/BF02948845

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  • DOI: https://doi.org/10.1007/BF02948845

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