Constraint satisfaction algorithms
Computational Intelligence
C4.5: programs for machine learning
C4.5: programs for machine learning
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Finding Temporal Relations: Causal Bayesian Networks vs. C4.5
ISMIS '00 Proceedings of the 12th International Symposium on Foundations of Intelligent Systems
Discovering Temporal Rules from Temporally Ordered Data
IDEAL '02 Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning
Discovering temporal/causal rules: a comparison of methods
AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
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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.