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
Aggrawa, C., and Yu, P. 1999. A new framework for itemset generation. Proceedings of the ACM Symposium on Principles of Database Systems, Seattle, USA, 18–24
Agrawal, R., Imielinski, T., and Swami, A. 1993. Mining association rules between sets of items in large databases. Proceedings of the 1993 ACM-SIGMOD International Conference on Management of Data, Washington, USA, 207–216
Agrawal, R., and Srikant, R. 1994. Fast algorithm for mining association rules. Proceedings of the 1994 International Conference on Very Large Databases, Santiago, Chile, 487–499
Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., and Verkamo, A. 1996. Fast discovery of association rules. In Fayyad, U., Piatesky-Shapiro, G., Smyth, P., and Uthurusamy, R. (Eds.), Advances in Knowledge Discovery and Data Mining, AAAI Press/The MIT Press, Menlo Park, CA, 307–328
Brin, S., Motwani, R., and Silverstein, C. 1997. Beyond market basket: generalizing association rules to correlations. Proceedings of the 1997 ACM-SIGMOD International Conference on Management of Data, Tuscon, USA, 265–276
Chen, M., Han, J., and Yu, P. 1996. Data mining: an overview from a database perspective. IEEE Transactions on Knowledge and Data Engineering, 8:866–883
Fukuda, T., Morimoto, Y., Morishita, S., and Tokuyama, T. 1996. Data mining using two-dimensional optimized association rules: scheme, algorithms and visualization. Proceedings of the 1996 ACM-SIGMOD International Conference on Management of Data, Montreal, Canada, 13–23
Han, J., and Fu, Y. 1995. Discovery of multiple-level association rules from large databases. Proceedings of the 1995 International Conference on Very Large Databases, Zurich, Swizerland, 420–431
Han, J., Pei, J., and Yin, Y. 2000. Mining frequent patterns without candidate generation. Proceedings of the 2000 ACM-SIGMOD International Conference on Management of Data, Dallas, USA, 1–12
Han, K., and Kamber, M. 2001. Data Mining: Concepts and Techniques, Morgan Kaufmann, San Fransico, USA
Kamber, M., Han, J., and Chiang, J. 1997. Metarule-guided mining of multi-dimensional association rules using data cubes. Proceedings of the 1997 International Conference on Knowledge Discovery and Data Mining, Newport Beach, CA, USA, 207–210
Kantardzic, M. 2002. Data Mining: Concepts, Models, Methods, and Algorithms, Wiley-IEEE Press, Piscataway, NJ, USA
Lent, B., Swami, A., and Widom, J. 1997. Clustering association rules. Proceedings of the 1997 International Conference on Data Engineering, Birmingham, England, 220–231
Mannila, H., Toivonen, H., and Verkamo, A. 1994. Efficient algorithms for discovering association rules. Proceedings of the AAAI’94 Workshop on Knowledge Discovery in Databases, Seattle, Washington, USA, 181–192
Park, J., Chen, M., and Yu, P. 1995. An effective hash-based algorithm for mining association rules. Proceedings of the 1995 ACM-SIGMOD International Conference on Management of Data, San Jose, CA, USA, 175–186
Savasere, A., Omiecinski, E., and Navathe, S. 1995. An efficient algorithm for mining association rules in large databases. Proceeding of the 1995 International Conference on Very Large Databases, Zurich, Switzerland, 432–443
Srikant, R., and Agrawal, R. 1995. Mining generalized association rules. Proceedings of the 1995 International Conference on Very Large Databases, Zurich, Switzerland, 407–419
Srikant, R., and Agrawal, R. 1996. Mining quantitative association rules in large relational tables. Proceedings of the 1996 ACM-SIGMOD International Conference on Management of Data, Montreal, Canada, 1–12
Tan, P-N., Steinbach, M., and Kumar, V. 2005. Introduction to Data Mining, Pearson Addison Wesley
Toivonen, H. 1996. Sampling large databases for association rules. Proceedings of the 1996 Conference on Very Large Databases, Bombay, India, 134–145
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2007 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Cios, K.J., Swiniarski, R.W., Pedrycz, W., Kurgan, L.A. (2007). Unsupervised Learning: Association Rules. In: Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-36795-8_10
Download citation
DOI: https://doi.org/10.1007/978-0-387-36795-8_10
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-33333-5
Online ISBN: 978-0-387-36795-8
eBook Packages: Computer ScienceComputer Science (R0)