Mining Quantitative Associations in Large Database

  • Chenyong Hu
  • Yongji Wang
  • Benyu Zhang
  • Qiang Yang
  • Qing Wang
  • Jinhui Zhou
  • Ran He
  • Yun Yan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3399)


Association Rule Mining algorithms operate on a data matrix to derive association rule, discarding the quantities of the items, which contains valuable information. In order to make full use of the knowledge inherent in the quantities of the items, an extension named Ratio Rules [6] is proposed to capture the quantitative association. However, the approach, which is addressed in [6], is mainly based on Principle Component Analysis (PCA) and as a result, it cannot guarantee that the ratio coefficient is non-negative. This may lead to serious problems in the association rules’ application. In this paper, a new method, called Principal Non-negative Sparse Coding (PNSC), is provided for learning the associations between itemsets in the form of Ratio Rules. Experiments on several datasets illustrate that the proposed method performs well for the purpose of discovering latent associations between itemsets in large datasets.


Association Rule Principal Component Analysis Association Rule Mining Nonnegative Matrix Factorization Latent Association 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Chenyong Hu
    • 1
  • Yongji Wang
    • 1
  • Benyu Zhang
    • 2
  • Qiang Yang
    • 3
  • Qing Wang
    • 1
  • Jinhui Zhou
    • 1
  • Ran He
    • 1
  • Yun Yan
    • 4
  1. 1.Lab for Internet Software Technologies, Institute of SoftwareChinese Academy of SciencesBeijing
  2. 2.Microsoft Research AsiaBeijingP.R. China
  3. 3.Department of Computer ScienceHong Kong University of Science and Technology 
  4. 4.LMAM, Department of Information Science, School of Mathematical SciencePeking UniversityBeijing

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