Abstract
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.
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References
Agrawal, R. and Srikant, S. (1994). Fast Algorithms for Mining Association Rules in Large Databases. In: VLDB’94, 487–499. Morgan Kaufmann.
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.
Jürgens, M. and Lenz, H.-J. (2001). Tree Based Indexes Versus Bitmap Indexes: A Performance Study. International Journal of Cooperative Information Systems, 10, 355–376.
Munakata, T. (1998). Rough Sets. In: Fundamentals of the New Artificial Intelligence, 140–182. New York: Springer-Verlag.
Pawlak, Z. (1982). Rough Sets. Int. J. Computer and Information Sci, 11, 341–356.
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© 2002 Springer-Verlag Berlin Heidelberg
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Delic, D., Lenz, HJ., Neiling, M. (2002). Rough Sets and Association Rules — Which is Efficient?. In: Härdle, W., Rönz, B. (eds) Compstat. Physica, Heidelberg. https://doi.org/10.1007/978-3-642-57489-4_81
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DOI: https://doi.org/10.1007/978-3-642-57489-4_81
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-1517-7
Online ISBN: 978-3-642-57489-4
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