Fourth IEEE International Conference on Data Mining (ICDM'04) (2004)
Brighton, United Kingdom
Nov. 1, 2004 to Nov. 4, 2004
Chenyong Hu , Institute of Software, CAS, Beijing, P.R. China
Benyu Zhang , Microsoft Research Asia, Beijing
Shuicheng Yan , LMAM, Peking University, Beijing, P.R. China
Qiang Yang , Hong Kong University of Science and Technology
Jun Yan , LMAM, Peking University, Beijing, P.R. China
Zheng Chen , Microsoft Research Asia, Beijing
Wei-Ying Ma , Microsoft Research Asia, Beijing
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.
W. Ma et al., "Mining Ratio Rules Via Principal Sparse Non-Negative Matrix Factorization," Fourth IEEE International Conference on Data Mining (ICDM'04)(ICDM), Brighton, United Kingdom, 2004, pp. 407-410.