Abstract
Mining association rules and correlation relationships have been studied in the data mining field for many years. However, the rules mined only indicate association relationships among variables in an interested system. They do not specify the essential underlying mechanism of the system that describe causal relationships. In this paper, we present an approach for mining causal relationships among attributes and propose a potential application in the field of bioinformatics. Based on the theory of causal diagram, we show the properties of our approach.
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References
Agrwal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD Conference on Management of Data, pp. 207–216 (1993)
Brin, S., Motwani, R., Silverstein, C.: Beyond Market Baskets: Generalizing Association Rules to Correlations. In: Proceedings of the ACM SIGMOD Conference on Management of Data, Tucson, AZ, pp. 267–276 (1997)
Friedman, N.: Inferring cellular networks using probabilistic graphical models. Science 303(5659), 799–805 (2004)
Cooper, G.F.: A Simple Constraint-based Algorithm for Efficiently Mining Observational Databases for Causal Relationships. Data Mining and Knowledge Discovery 1, 203–224 (1997)
Hecherman, D.: Bayesian Networks for Data Mining. Data Mining and Knowledge Discovery 1, 79–119 (1997)
Jansen, R., Yu, H.Y., Greenbaum, D.: A Bayesian networks approach for predicting protein-protein interactions from genomic data. Science 302(5644), 449–453 (2003)
Lauritzen, S.L.: Graphical Models. Clarendon Press, Oxford (1996)
Piatetsky, S.G., Frawley, W.J.: Knowledge Discovery in Databases. AAAI/MIT Press, Cambridge (1991)
Spirtes, P., Glymour, C., Scheines, R.: Causation, Prediction and Search. Springer, New York (1993)
Verstein, C.S., Brin, S., Motwani, R., Ullman, J.: Scalable Techniques for Mining Causal Structures. Data Mining and Knowledge Discovery 4, 163–192 (2000)
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He, Y.B., Geng, Z., Liang, X. (2005). An Approach to Mining Local Causal Relationships from Databases. In: Li, X., Wang, S., Dong, Z.Y. (eds) Advanced Data Mining and Applications. ADMA 2005. Lecture Notes in Computer Science(), vol 3584. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527503_8
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DOI: https://doi.org/10.1007/11527503_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-27894-8
Online ISBN: 978-3-540-31877-4
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