Abstract
Discovering causal relations in a system is essential to understanding how it works and to learning how to control the behaviour of the system. RFCT is a causality miner that uses association relations as the basis for the discovery of causal relations. It does so by making explicit the temporal relationships among the data. RFCT uses C4.5 as its association discoverer, and by using a series of pre-processing and post-processing techniques enables the user to try different scenarios for mining causality. The raw data to be mined should originate from a single system over time. RFCT expands the abilities of C4.5 in some important ways. It is an unsupervised tool that can handle and interpret temporal data. It also helps the user in analyzing the relationships among the variables by enabling him/her to see the rules, and statistics about them, in tabular form. The user is thus encouraged to perform experiments and discover any causal or temporal relationships among the data.
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
Freedman, D. and Humphreys, P., Are There Algorithms that Discover Causal Structure?, Technical Report 514, Department of Statistics, University of California at Berkeley, 1998.
Karimi, K. and Hamilton, H.J., Finding Temporal Relations: Causal Bayesian Networks vs. C4.5, The Twelfth International Symposium on Methodologies for Intelligent Systems (ISMIS’2000), Charlotte, NC, USA, October 2000.
Karimi, K. and Hamilton, H.J., Learning With C4.5 in a Situation Calculus Domain, The Twentieth SGES International Conference on Knowledge Based Systems and Applied Artificial Intelligence (ES2000), Cambridge, UK, December 2000.
Karimi, L. and Hamilton, H.J., Temporal Rules and Temporal Decision trees: A C4.5 Approach, Technical Report CS-2001-02, Department of Computer Science, University of Regina, Regina, Saskatchewan, Canada, December 2001.
Korb, K. B. and Wallace, C. S., In Search of Philosopher’s Stone: Remarks on Humphreys and Freedman’s Critique of Causal Discovery, British Journal of the Philosophy of Science 48, pp. 543–553, 1997.
Nadel, B.A., Constraint Satisfaction Algorithms, Computational Intelligence, No. 5, 1989.
Pearl, J., Causality: Models, Reasoning, and Inference, Cambridge University Press. 2000.
Quinlan, J. R., C4.5: Programs for Machine Learning, Morgan Kaufmann, 1993.
Spirtes, P. and Schemes, R., Reply to Freedman, In McKim, V. and Turner, S. (editors), Causality in Crisis, University of Notre Dame Press, pp. 163–176, 1997.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Karimi, K., Hamilton, H.J. (2002). RFCT: An Association-Based Causality Miner. In: Cohen, R., Spencer, B. (eds) Advances in Artificial Intelligence. Canadian AI 2002. Lecture Notes in Computer Science(), vol 2338. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-47922-8_29
Download citation
DOI: https://doi.org/10.1007/3-540-47922-8_29
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-43724-6
Online ISBN: 978-3-540-47922-2
eBook Packages: Springer Book Archive