Mining Ratio Rules Via Principal Sparse Non-Negative Matrix Factorization

I
ICDM
2004
Fourth IEEE International Conference on Data Mining (ICDM'04)
Abstract - Mining Ratio Rules Via Principal Sparse Non-Negative Matrix Factorization

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Fourth IEEE International Conference on Data Mining (ICDM'04)

Brighton, United Kingdom

November 01-November 04

ISBN: 0-7695-2142-8

	ASCII Text	x
	Chenyong Hu, Benyu Zhang, Shuicheng Yan, Qiang Yang, Jun Yan, Zheng Chen, Wei-Ying Ma, "Mining Ratio Rules Via Principal Sparse Non-Negative Matrix Factorization," Data Mining, IEEE International Conference on, pp. 407-410, Fourth IEEE International Conference on Data Mining (ICDM'04), 2004.

	BibTex	x
	@article{ 10.1109/ICDM.2004.10062, author = {Chenyong Hu and Benyu Zhang and Shuicheng Yan and Qiang Yang and Jun Yan and Zheng Chen and Wei-Ying Ma}, title = {Mining Ratio Rules Via Principal Sparse Non-Negative Matrix Factorization}, journal ={Data Mining, IEEE International Conference on}, volume = {0}, year = {2004}, isbn = {0-7695-2142-8}, pages = {407-410}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2004.10062}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Data Mining, IEEE International Conference on TI - Mining Ratio Rules Via Principal Sparse Non-Negative Matrix Factorization SN - 0-7695-2142-8 SP407 EP410 A1 - Chenyong Hu, A1 - Benyu Zhang, A1 - Shuicheng Yan, A1 - Qiang Yang, A1 - Jun Yan, A1 - Zheng Chen, A1 - Wei-Ying Ma, PY - 2004 KW - null VL - 0 JA - Data Mining, IEEE International Conference on ER -

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

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2004.10062

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.

Citation:

Chenyong Hu, Benyu Zhang, Shuicheng Yan, Qiang Yang, Jun Yan, Zheng Chen, Wei-Ying Ma, "Mining Ratio Rules Via Principal Sparse Non-Negative Matrix Factorization," icdm, pp.407-410, Fourth IEEE International Conference on Data Mining (ICDM'04), 2004

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