learning quantifiable associations via principal sparse non-negative matrix factorization
文献类型：期刊论文
| 作者 | Hu Chenyong ; Zhang Benyu ; Wang Yongji ; Yan Shuicheng ; Chen Zheng ; Wang Qing ; Yang Qiang |
| 刊名 | INTELLIGENT DATA ANALYSIS
 |
| 出版日期 | 2005 |
| 卷号 | 9期号:6页码:603-620 |
| 关键词 | data mining association rules non-negative matrix factorization principal sparse non-negative matrix factorization |
| ISSN号 | 1088-467X |
| 学科主题 | Computer Science, Artificial Intelligence |
| 收录类别 | SCI |
| 语种 | 英语 |
| 公开日期 | 2011-07-28 |
| 附注 | Association rules are traditionally designed to capture statistical relationship among itemsets in a given database. To additionally capture the quantitative association knowledge, Korn et. al. recently propose a paradigm named Ratio Rules 6 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 coefficients are 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. Experiments on several datasets illustrate that the proposed method performs well for discovering latent associations between itemsets in large datasets. |
| 源URL | [http://124.16.136.157/handle/311060/12482]  |
| 专题 | 软件研究所_软件所图书馆_期刊论文 |
| 推荐引用方式 GB/T 7714 | Hu Chenyong,Zhang Benyu,Wang Yongji,et al. learning quantifiable associations via principal sparse non-negative matrix factorization[J]. INTELLIGENT DATA ANALYSIS,2005,9(6):603-620. |
| APA | Hu Chenyong.,Zhang Benyu.,Wang Yongji.,Yan Shuicheng.,Chen Zheng.,...&Yang Qiang.(2005).learning quantifiable associations via principal sparse non-negative matrix factorization.INTELLIGENT DATA ANALYSIS,9(6),603-620. |
| MLA | Hu Chenyong,et al."learning quantifiable associations via principal sparse non-negative matrix factorization".INTELLIGENT DATA ANALYSIS 9.6(2005):603-620. |
入库方式： OAI收割
来源：软件研究所
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