Abstract
Association Rule Mining algorithms operate on a data matrix to derive association rule, discarding the quantities of the items, which contains valuable information. In order to make full use of the knowledge inherent in the quantities of the items, an extension named Ratio Rules [6] is proposed to capture the quantitative association. However, the approach, which is addressed in [6], 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 association rules’ application. In this paper, a new method, called Principal Non-negative Sparse Coding (PNSC), is provided for learning the associations between itemsets in the form of Ratio Rules. Experiments on several datasets illustrate that the proposed method performs well for the purpose of discovering latent associations between itemsets in large datasets.
Supported by the National Natural Science Foundation of China under Grant No.60373053, 60273026;the hundred Talents of the Chinese Academy of Sciences; the National High-Tech Research and Development Plan of China (863)under Grant No. 2002AA116080.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Agrawal, R., Imielinski, T., Swami, A.N.: Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD , Washington, pp. 207–216 (1993)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of VLDB 1994, Santiago,Chile, pp. 487–499 (1994)
Aumann, Y., Lindell, Y.: A statistical theory for quantitative association rules. In: Proceedings of KDD, pp. 261–270 (1999)
Guan, J.W., Bell, D.A., Liu, D.Y.: The Rough Set Approach to Association Rule Mining. In: Proceedings of the Third IEEE International Conference on Data Mining, Melbourne, Florida, pp. 529–532 (2003)
Han, J., Fu, Y.: Discovery of Multiple-Level Association Rules from Large Databases. in: Proceedings of the VLDB 1995, 420–431 (1995)
Korn, F., Labrinidis, A., Kotidis, Y., Faloutsos, C.: Quantifiable Data Mining Using Principal Component Analysis. In: Proceedings of VLDB, pp. 582–593 (1998)
Lee, D.D., Seung, H.S.: Algorithms for nonnegative matrix factorization. In: Proceedings of the Advances in Neural Information Processing Systems, vol. 13, pp. 556–562. MIT Press, Cambridge (2001)
Li, W., Han, J., Pe, J.: CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules. In: Proceedings of the 2001 Int. Conf. on Data Mining (ICDM 2001), San Jose, CA, pp. 369–376 (2001)
Lin, J., Dunham, M.H.: Mining association rules: Anti-skew algorithms. In: Proceedings of the Intl. Conf. on Data Engineering, pp. 486–493 (1998)
Otey, M.E., Wang, C., Parthasarathy, S., et al.: Mining Frequent Itemsets in Distributed and Dynamic Databases. In: Proceedings of the ICDM 2003, pp. 617–620 (2003)
Kaya, M., Alhajj, R.: Facilitating Fuzzy Association Rules Mining by Using Multi-Objective Genetic Algorithms for Automated Clustering. In: Proceedings of the ICDM 2003, pp. 561–564 (2003)
Ng, E.K.K., Fu, A.W.-C., Wang, K.: Mining Association Rules from Stars. In: Proceedings of the 2nd IEEE Intl. Conf. on Data Mining, Maebashi, Japan, pp. 322–329 (2002)
Olshausen, B.A., Field, D.J.: Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381, 607–609 (1996)
Patrik, O.: Hoyer. Non-negative sparse coding. In: Proc. IEEE Workshop on Neural Networks for Signal Processing, Martigny, Switzerland (2002)
Schuster, A., Wolff, R., Trock, D.: A High-Performance Distributed Algorithm for Mining Association Rules. In: Proceedings of the ICDM 2003, pp. 291–298 (2003)
Srikant, R., Agrawal, R.: Mining quantitative association rules in large relational tables. In: Proceedings of the the ACM SIGMOD international conference on Management of data, pp. 1–12 (1996)
Wang, H., Perng, C.S., Ma, S., Yu, P.S.: Mining Associations by Pattern Structure in Large Relational Tables. In: Proceedings of the 2nd IEEE Intl. Conf. on Data Mining, Maebashi, Japan, pp. 482–489 (2002)
Wolff, R., Schuster, A.: Association Rule Mining in Peer-to-Peer Systems. In: Proceedings of the ICDM 2003, pp. 291–298 (2003)
Zaki, M.J., Parthasarathy, S., Ogihara, M., Li, W.: New parallel algorithms for fast discovery of association rules. Data Mining and Knowledge Discovery: An International Journal 4(1), 343–373 (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hu, C. et al. (2005). Mining Quantitative Associations in Large Database. In: Zhang, Y., Tanaka, K., Yu, J.X., Wang, S., Li, M. (eds) Web Technologies Research and Development - APWeb 2005. APWeb 2005. Lecture Notes in Computer Science, vol 3399. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-31849-1_40
Download citation
DOI: https://doi.org/10.1007/978-3-540-31849-1_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25207-8
Online ISBN: 978-3-540-31849-1
eBook Packages: Computer ScienceComputer Science (R0)