Abstract
In this paper, two concepts are introduced: decision correlation rules and contingency vectors. The first concept results from a cross fertilization between correlation and decision rules. It enables relevant links to be highlighted between sets of patterns of a binary relation and the values of target items belonging to the same relation on the twofold basis of the Chi-Squared measure and of the support of the extracted patterns. Due to the very nature of the problem, levelwise algorithms only allow extraction of results with long execution times and huge memory occupation. To offset these two problems, we propose an algorithm based both on the lectic order and contingency vectors, an alternate representation of contingency tables.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., Verkamo, A.I.: Fast Discovery of Association Rules. In: Advances in Knowledge Discovery and Data Mining, pp. 307–328 (1996)
Brin, S., Motwani, R., Silverstein, C.: Beyond market baskets: generalizing association rules to correlations. In: Proceedings of the International Conference on Management of Data, SIGMOD, pp. 265–276 (1997)
Casali, A., Ernst, C.: Extracting correlated sets using the chi-squared measurement within n-ary relations: An implementation. In: 8th European Advanced Equipement Control / Advanced Process Control, AEC/APC, 4 p. (2007)
Flouvat, F., De Marchi, F., Petit, J.M.: A thorough experimental study of datasets for frequent itemsets. In: Proceedings of the 5th International Conference on Data Mining, ICDM, pp. 162–169 (2005)
Ganter, B., Wille, R.: Formal Concept Analysis: Mathematical Foundations. Springer, Heidelberg (1999)
Grahne, G., Lakshmanan, L., Wang, X.: Efficient Mining of Constrained Correlated Sets. In: Proceedings of the 16th International Conference on Data Engineering, ICDE, pp. 512–524 (2000)
Hirsh, H.: Generalizing version spaces. Machine Learning 17(1), 5–46 (1994)
Laporte, M., Novelli, N., Cicchetti, R., Lakhal, L.: Computing full and iceberg datacubes using partitions. In: Hacid, M.-S., Raś, Z.W., Zighed, D.A., Kodratoff, Y. (eds.) ISMIS 2002. LNCS, vol. 2366, pp. 244–254. Springer, Heidelberg (2002)
Laurent, D., Spyratos, N.: Partition semantics for incomplete information in relational databases. In: Proceedings of the International Conference on Management of Data, SIGMOD, pp. 66–73 (1988)
Mannila, H., Toivonen, H.: Levelwise Search and Borders of Theories in Knowledge Discovery. Data Mining and Knowledge Discovery 1(3), 241–258 (1997)
Moore, D.S.: Tests of chi-squared type. In: D’Agostino, R.B., Stephens, M.A. (eds.) Goodness-of-Fit Techniques, pp. 63–95. Marcel Dekker, New York (1986)
Pyle, D.: Data Preparation for Data Mining. Morgan Kaufmann, San Francisco (1999)
Spiegel, M.R.: Théorie et applications de la statistique, Schaum (1990)
Vel, M.: Theory of Convex Structures. North-Holland, Amsterdam (1993)
Wu, X., Zhang, C., Zhang, S.: Efficient mining of both positive and negative association rules. ACM Trans. Inf. Syst. 22(3), 381–405 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Casali, A., Ernst, C. (2009). Extracting Decision Correlation Rules. In: Bhowmick, S.S., Küng, J., Wagner, R. (eds) Database and Expert Systems Applications. DEXA 2009. Lecture Notes in Computer Science, vol 5690. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03573-9_57
Download citation
DOI: https://doi.org/10.1007/978-3-642-03573-9_57
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03572-2
Online ISBN: 978-3-642-03573-9
eBook Packages: Computer ScienceComputer Science (R0)