Abstract
A notion of legitimate definitions of support and confidence under incompleteness is defined. Properties of generic legitimate definitions of support and confidence are investigated. We show that in the case of incompleteness legitimate association rules can be derived from legitimate representative rules by the cover operator. It is proved that the minimum condition maximum consequence association rules under incompleteness constitute a subset of representative rules of the same type. Algorithms for generating association rules under incompleteness are offered.
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References
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) 207–216
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, CA (1996) 307–328
Kryszkiewicz, M.: Representative Association Rules. In: Proc. of PAKDD’ 98. Melbourne, Australia. LNAI 1394. Springer-Verlag (1998) 198–209
Kryszkiewicz M.: Representative Association Rules and Minimum Condition Maximum Consequence Association Rules. In: Proc. of PKDD’ 98. Nantes, France. LNAI 1510. Springer-Verlag (1998) 361–369
Kryszkiewicz, M.: Association Rules in Incomplete Databases: In: Proc. of PAKDD’ 99. Beijing, China. LNAI 1574. Springer-Verlag (1999) 84–93
Kryszkiewicz, M.: Probabilistic Approach to Association Rules in Incomplete Databases, Proc. of WAIM’ 2000. Shanghai, Chiny. LNCS1846. Springer-Verlag (2000), 133–138
Kryszkiewicz M., Rybi·ski H.: Incomplete Database Issues for Representative Association Rules. In: Proc. ISMIS’ 99. Warsaw, Poland. LNAI 1609. Springer (1999]) 583–591
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) 432–444
Toivonen H., Klemettinen M., Ronkainen P., Hätönen K., and H. Mannila H.: Pruning and grouping discovered association rules. In: MLnet Workshop on Statistics, Machine Learning, and Discovery in Databases. Heraklion, Crete, Greece (1995) 47–52
Washio, T., Matsuura, H., Motoda, H.: Mining Association Rules for Estimation and Prediction. In: Proc. of PAKDD’ 98. Melbourne, Australia. LNAI 1394. Springer-Verlag (1998) 417–419
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Kryszkiewicz, M., Rybinski, H. (2000). Legitimate Approach to Association Rules under Incompleteness. In: Raś, Z.W., Ohsuga, S. (eds) Foundations of Intelligent Systems. ISMIS 2000. Lecture Notes in Computer Science(), vol 1932. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-39963-1_53
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DOI: https://doi.org/10.1007/3-540-39963-1_53
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