Learning quantifiable associations via principal sparse non-negative matrix factorization

Authors:
Chenyong Hu;Benyu Zhang;Yongji Wang;Shuicheng Yan;Zheng Chen;Qing Wang;Qiang Yang
Affiliations:
Lab for Internet Software Technologies, Institute of Software Chinese Academy of Sciences, Beijing, 100080, P.R. China. E-mail: {huchenyong,ywang,wq}@itechs.iscas.ac.cn;Microsoft Research Asia, 49 Zhichun Road, Beijing, 100080, P.R. China. E-mail: {byzhang,zhengc}@microsoft.com;Lab for Internet Softw. Technol., Inst. of Softw. Chin. Acad. of Sci., Beijing, 100080, P.R. China. E-mail: {huchenyong,ywang,wq}@itechs.iscas.ac.cn and Key Lab of Comp. Sci., Inst. of Softw. Chin ...;Department of Information Engineering, the Chinese University of Hong Kong, Hong Kong. E-mail: scyan@ie.cuhk.edu.hk;Microsoft Research Asia, 49 Zhichun Road, Beijing, 100080, P.R. China. E-mail: {byzhang,zhengc}@microsoft.com;Lab for Internet Software Technologies, Institute of Software Chinese Academy of Sciences, Beijing, 100080, P.R. China. E-mail: {huchenyong,ywang,wq}@itechs.iscas.ac.cn;Department of Computer Science, Hong Kong University of Science and Technology. E-mail: qyang@cs.ust.hk
Venue:
Intelligent Data Analysis
Year:
2005

Citing 19
Cited 1

Mining association rules between sets of items in large databases

SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining quantitative association rules in large relational tables

SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
A statistical theory for quantitative association rules

KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Unsupervised learning by probabilistic latent semantic analysis

Machine Learning
Parallel Algorithms for Discovery of Association Rules

Data Mining and Knowledge Discovery
Using a Hash-Based Method with Transaction Trimming for Mining Association Rules

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Mining Association Rules: Anti-Skew Algorithms

ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules

ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Ratio Rules: A New Paradigm for Fast, Quantifiable Data Mining

VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases

VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovery of Multiple-Level Association Rules from Large Databases

VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
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Mining Association Rules from Stars

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Facilitating Fuzzy Association Rules Mining by Using Multi-Objective Genetic Algorithms for Automated Clustering

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The Rough Set Approach to Association Rule Mining

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A High-Performance Distributed Algorithm for Mining Association Rules

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Association Rule Mining in Peer-to-Peer Systems

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Narrowing the semantic gap - improved text-based web document retrieval using visual features

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Models for association rules based on clustering and correlation

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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, 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.