Mining ratio rules via principal sparse non-negative matrix factorization (2004)
| Venue: | in Proc. IEEE Int. Conf. Data Mining |
| Citations: | 7 - 0 self |
BibTeX
@INPROCEEDINGS{Hu04miningratio,
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},
booktitle = {in Proc. IEEE Int. Conf. Data Mining},
year = {2004},
pages = {407--410}
}
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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 [4] 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 Factoriza-tion (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. 1.
Citations
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| 23 | Ratio rules: A new paradigm for fast, quanti able data mining - Korn, Labrinidis, et al. |
