Abstract
Association rules discovered through attribute-oriented induction are commonly used in data mining tools to express relationships between variables. However, causal inference algorithms discover more concise relationships between variables, namely, relations of direct cause. These algorithms produce regressive structured equation models for continuous linear data and Bayes networks for discrete data. This work compares the effectiveness of causal inference algorithms with association rule induction for discovering patterns in discrete data.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Data Mining Research Group, School of Computing Science (1999). DBMiner E1.1 (Beta 1) User Manual. Simon Fraser University, Vancouver, British Columbia.
Fayyad, U., Piatetsky-Shapiro G., & Smyth P. (1996, November). The KDD Process for Extracting Useful Knowledge from Volumes of Data. Communications of the ACM, pp. 27–34.
Freedman, D., & Humphreys P. (1998). Are there Algorithms that Discover Causal Structure. University of California at Berkeley.
Han J., Chiang J., Chee, S., Chen, J., Cheng S., Gong, W., Kamber, M., Koperski K., Liu, G., Lu, Y., Sefanovic, N., Winstone, L., Xia, B., Zaiane, O., & Zhu, H. (1997, November). DBMiner: A System for Data Mining in Relational Databases and Data Warehouses. In Proceedings of CASCON 1997. CASCON’97. Toronto, Canada.
Humphreys P., & Freedman, D. (1996). The Grand Leap. British Journal of the Philosophy of Science, 47 (113–23).
Pearl, J. Causal Diagrams for Empirical Research. (1995) Biometrika, 82:4, 669–P
Pearl J., & Verma, T. (1991). A theory of inferred causation. Allen J.A., Fikes R. & Sandewall E (Eds.). Principles of Knowledge Representation and Reasoning: Proceedings of the 2nd International Conference. San Mateo, CA.: Morgan Kaufmann.
Scheines R., Spirtes P., Glymour C., Meek C., &. (1994). Tetrad II Tools for Causal Modeling. Hillsdale, NJ: Lawrence Erlbaum Associates, Inc.
Spirtes P., Glymour C., & Scheines R. (1993). Causation, Prediction and Search. Lecture Notes in Statistics, vol. 81. New York: Springer-Verlag.
Spirtes P., & Scheines R. (1997). Reply to Freedman. In McKim V. & Turner S. (Eds.), Causality in Crisis (pp. 163–176). University of Notre Dame Press.
Westphal C, & Blaxton T. (1998). Data Mining Solutions. New York: John Wiley & Sons, Inc.
Zweig, G. and Russell, S. (1998) Speech Recognition with Dynamic Bayesian Networks. In Proceedings of AAAI-98, Madison, Wisconsin (pp 173–180)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bowes, J., Neufeld, E., Greer, J.E., Cooke, J. (2000). A Comparison of Association Rule Discovery and Bayesian Network Causal Inference Algorithms to Discover Relationships in Discrete Data. In: Hamilton, H.J. (eds) Advances in Artificial Intelligence. Canadian AI 2000. Lecture Notes in Computer Science(), vol 1822. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45486-1_27
Download citation
DOI: https://doi.org/10.1007/3-540-45486-1_27
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-67557-0
Online ISBN: 978-3-540-45486-1
eBook Packages: Springer Book Archive