Mining Ratio Rules Via Principal Sparse Non-Negative Matrix Factorization

  • Authors:

  • Chenyong Hu;Benyu Zhang;Shuicheng Yan;Qiang Yang;Jun Yan;Zheng Chen;Wei-Ying Ma

  • Affiliations:

  • Institute of Software, CAS, Beijing, P.R. China;Microsoft Research Asia, Beijing;LMAM, Peking University, Beijing, P.R. China;Hong Kong University of Science and Technology;LMAM, Peking University, Beijing, P.R. China;Microsoft Research Asia, Beijing;Microsoft Research Asia, Beijing

  • Venue:
  • ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
  • Year:
  • 2004

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Abstract

Association rules are traditionally designed to capture statistical relationship among itemsets in a given database. To additionally capture the quantitative association knowledge, F.Korn et al recently proposed a paradigm named Ratio Rules for quantifiable data mining. However, their approach 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 rules' application. In this paper, we propose a new method, called Principal Sparse Non-Negative Matrix Factorization (PSNMF), for learning the associations between itemsets in the form of Ratio Rules. In addition, we provide a support measurement to weigh the importance of each rule for the entire dataset.