Mining around Association and Representative Rules
Discovering association rules among items in large databases is a recognized database mining problem. In the paper, we address the issue of association rules generation in the context of changing user requirements. The data mining system user is frequently interested in results of mining around rules that consists in examining how change of attribute values or addition of new attributes influences the discovered dependencies. The set of association rules is often huge. However it is possible to represent it with usually much smaller set of representative rules. If needed, the user can derive all association rules from the set of representative rules syntactically by means of a cover operator. Incremental solutions of mining around representative rules are discussed in the paper as well.
Unable to display preview. Download preview PDF.
- 1.Agrawal, R., Imielinski, T., Swami, A.: Mining Associations Rules between Sets of Items in Large Databases. In: Proc. of the ACM SIGMOD Conference on Management of Data. Washington, D.C. (1993) 207–216Google Scholar
- 2.Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., Verkamo, A.I.: Fast Discovery of Association Rules. In: Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R. (eds.): Advances in Knowledge Discovery and Data Mining. AAAI, Menlo Park, California (1996) 307–328Google Scholar
- 3.Gajek, M.: Comparative Analysis of Selected Association Rules Types. To appear in Proc. of the Ninth International Symposium on Intelligent Information Systems (2000)Google Scholar
- 4.Kryszkiewicz, M.:Representative Association Rules. In: Proc. of PAKDD’ 98. Melbourne, Australia. Lecture Notes in Artificial Intelligence1394. Research and Development in Knowledge Discovery and Data Mining. Springer-Verlag (1998) 198–209Google Scholar
- 5.Kryszkiewicz, M.: Fast Discovery of Representative Association Rules. In: Proc. of RSCTC’ 98. Warsaw, Poland. Lecture Notes in Artificial Intelligence 1424. Rough Sets and Current Trends in Computing. Springer-Verlag (1998) 214–221Google Scholar
- 6.Meo, R., Psaila, G., Ceri, S.: A New SQL-like Operator for Mining Association Rules. In: Proc. of the 22nd VLDB Conference. Mumbai (Bombay), India (1996)Google Scholar
- 7.Savasere, A, Omiecinski, E., Navathe, S.: An Efficient Algorithm for Mining Association Rules in Large Databases. In: Proc. of the 21st VLDB Conference. Zurich, Swizerland (1995) 432–444Google Scholar
- 8.Srikant, R., Agrawal, R.: Mining Generalized Association Rules. In: Proc. of the 21st VLDB Conference. Zurich, Swizerland (1995) 407–419Google Scholar
- 9.Walczak, Z.: Selected Problems and Algorithms of Data Mining. M.Sc. Thesis. Warsaw University of Technology (in Polish) (1998)Google Scholar