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Causal Rule Analysis

Chapter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2307)

Abstract

Causal rules attached to matrices can be used to capture causal rela- tionships among multi-value variables in data. However, because the causal relations are represented in a non-linear form (a matrix), it is rather difficult to make decisions using the causal rules. Therefore, one of the main challenges is to reduce the complexity of the repre- sentation. As important research into post data mining, this chapter firstly establishes a method of optimizing causal rules which tackles the ‘useless’ information in the conditional probability matrices of the extracted rules. Then, techniques for constructing polynomial functions for approximate causality in data are advocated. Finally, we propose an approach for finding the approximate polynomial causal- ity between two variables from a given data set by fitting.

Keywords

Bayesian Network Polynomial Function Approximation Function Identical Property Encoder Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

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