Abstract
Interesting patterns can be revealed by applying knowledge discovery processes in data warehouses. However, the existing data mining techniques only allow one to extract patterns from a single fact table of a data warehouse. Since each fact table contains data about a subject, the existing techniques do not allow multiple subjects of a data warehouse to be related. In this paper, we propose a new technique for mining association rules in a data warehouse, which allows items from multiple subjects of a data warehouse to be related. The rules mined through this technique are called multifact association rules because they relate items from multiple fact tables. We propose a new efficient algorithm called Connection to mine such rules. The proposed algorithm can process each fact table in parallel, resulting in improved performance.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proc. of the ACM SIGMOD Int’l Conf. on Management of Data, Washington, D.C., USA (1993)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proc. of the Int’l Conf. on Very Large Databases, Santiago de Chile, Chile (1994)
Bayardo, R., Agrawal, R.: Mining the most interesting rules. In: Proc. of the ACM SIGKDD Int’l Conf. on Knowledge Discovery and Data Mining, San Diego, CA, USA (1999)
Deshape, L., Raedt, L.: Mining association rules in multiple relations. In: Proc. of the 7th International Workshop on Inductive Logic Programming, Prague, Czech Republic (1997)
Dong, G., Li, J.: Interestingness of Discovered Association Rules in Terms of Neighborhood- based Unexpectedness. In: Wu, X., Kotagiri, R., Korb, K.B. (eds.) PAKDD 1998. LNCS, vol. 1394, Springer, Heidelberg (1998)
Dzeroski, S., Raedt, L.: Multi-relational Data Mining: a Workshop Report. ACM SIGKDD Explorations Newsletter, vol. 4 (2002)
Elmasri, R., Navathe, S.: Fundamentals of Database Systems. Addison-Wesley, Reading (2000)
Freitas, A.: On Objective Measures of Rule Surprisingness. In: Żytkow, J.M. (ed.) PKDD 1998. LNCS, vol. 1510, Springer, Heidelberg (1998)
Freitas, A.: On rule interestingness measures. Knowledge-Based Systems 12 (1999)
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: Proc. of the ACM SIGMOD Int’l Conf. on Management of Data, Dallas, Texas, USA (2000)
Hilderman, R.J., Hamilton, H.J.: Knowledge discovery and interestingness measures: a survey. Technical Report CS 99-04, Department of Computer Science, University of Regina, Canada (1999)
Hipp, J., Güntzer, U., Nakhaeizadeh, G.: Algorithms for Association Rule Mining - A General Survey and Comparison. SIGKDD Explorations 2 (2000)
Kimball, R.: Data Warehouse Architect, in DBMS and Internet System, pp. 14–19 (1996), http://www.dbmsmag.com/9603d05.html
Nijssen, S., Kok, J.: Faster Association Rule for Multiple Relations. In: In Proc. of the 7th International Joint Conference on Artificial Intelligence (IJCAI 2001), Seattle, USA (2001)
Omiecinski, E.: Alternative interest measures for mining associations in databases. IEEE Transactions on Knowledge and Data Engineering 15(1) ( January/February 2003)
Savarese, A., Omiecinski, E., Navathe, S.: An Efficient Algorithm for Mining Association Rules in Large Databases. In: Proc. of the 21st Conf. on Very Large Databases, VLDB 1995 (1995)
Vaidya, J.S., Clifton, C.: Privacy Preserving Association Rule Mining in Vertically Partitioned Data. In: Proc. of the 8th ACM SIGKDD Int’l Conf. on Knowlegde Discovery and Data Mining, Edmonton, Alberta, Canada (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ribeiro, M.X., Pires Vieira, M.T. (2004). A New Approach for Mining Association Rules in Data Warehouses. In: Christiansen, H., Hacid, MS., Andreasen, T., Larsen, H.L. (eds) Flexible Query Answering Systems. FQAS 2004. Lecture Notes in Computer Science(), vol 3055. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25957-2_9
Download citation
DOI: https://doi.org/10.1007/978-3-540-25957-2_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22160-9
Online ISBN: 978-3-540-25957-2
eBook Packages: Springer Book Archive