Mining Ratio Rules via principal sparse non-negative matrix factorization

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

doi:10.1109/icdm.2004.10062

Mining Ratio Rules via principal sparse non-negative matrix factorization

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

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

14 Citations (Scopus)

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. © 2004 IEEE.

Original language	English
Title of host publication	Proceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004
Pages	407-410
DOIs	https://doi.org/10.1109/icdm.2004.10062
Publication status	Published - 2004
Externally published	Yes
Event	Proceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004 - Brighton, United Kingdom Duration: 1 Nov 2004 → 4 Nov 2004

Publication series

Name	Proceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004

Conference

Conference	Proceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004
Place	United Kingdom
City	Brighton
Period	1/11/04 → 4/11/04

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

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title = "Mining Ratio Rules via principal sparse non-negative matrix factorization",

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. {\textcopyright} 2004 IEEE.",

author = "Chenyong Hu and Benyu Zhang and Shuicheng Yan and Qiang Yang and Jun Yan and Zheng Chen and Wei-Ying Ma",

note = "Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].; Proceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004 ; Conference date: 01-11-2004 Through 04-11-2004",

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doi = "10.1109/icdm.2004.10062",

language = "English",

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series = "Proceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004",

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booktitle = "Proceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004",

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Hu, C, Zhang, B, Yan, S, Yang, Q, Yan, J, Chen, Z & Ma, W-Y 2004, Mining Ratio Rules via principal sparse non-negative matrix factorization. in Proceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004. Proceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004, pp. 407-410, Proceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004, Brighton, United Kingdom, 1/11/04. https://doi.org/10.1109/icdm.2004.10062

Mining Ratio Rules via principal sparse non-negative matrix factorization. / Hu, Chenyong; Zhang, Benyu; Yan, Shuicheng et al.
Proceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004. 2004. p. 407-410 (Proceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

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AU - Hu, Chenyong

AU - Zhang, Benyu

AU - Yan, Shuicheng

AU - Yang, Qiang

AU - Yan, Jun

AU - Chen, Zheng

AU - Ma, Wei-Ying

N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

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N2 - 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. © 2004 IEEE.

AB - 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. © 2004 IEEE.

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T3 - Proceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004

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BT - Proceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004

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ER -

Mining Ratio Rules via principal sparse non-negative matrix factorization

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