Improving the Quality of Association Rule Mining by Means of Rough Sets
We evaluate the rough set and the association rule method with respect to their performance and the quality of the produced rules. It is shown that despite their different approaches, both methods are based on the same principle and, consequently, must generate identical rules. However, they differ strongly with respect to performance Subsequently an optimized association rule procedure is presented which unifies the advantages of both methods.
KeywordsAssociation Rule Minimum Support Association Rule Mining Decision Attribute Rule Derivation
Unable to display preview. Download preview PDF.
- 1.Agrawal, R., Imielinski, T., Swami, A. (1993). Mining Association Rules between Sets of Items in Large Databases. In: Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, Washington D.C., USA. 207216. ACM Press.Google Scholar
- 2.Agrawal, R. and Srikant, S. (1994). Fast Algorithms for Mining Association Rules in Large Databases. In: VLDB’94, 487–499. Morgan Kaufmann.Google Scholar
- 3.Delic, D. (2001). Data Mining-Abhängigkeitsanalyse von Attributen mit Assoziationsregeln und Rough Sets. MS thesis. Free University of Berlin, Institute of Applied Computer Science, Berlin, Germany.Google Scholar
- 5.Munakata, T. (1998). Rough Sets. In: Fundamentals of the New Artificial Intelligence, 140–182. New York: Springer-Verlag.Google Scholar
- 7.Skowron, A. and Rauszer, C. (1992). The discernibility matrices and functions in information systems. In R. Slowinski (ed.): Intelligent Decision Support. Handbook of Applications and Advances of Rough Sets Theory, 331–362, Dordrecht: Kluwer.Google Scholar