C4.5: programs for machine learning
C4.5: programs for machine learning
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Scalable Techniques for Mining Causal Structures
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Logical Decision Rules: Teaching C4.5 to Speak Prolog
IDEAL '00 Proceedings of the Second International Conference on Intelligent Data Engineering and Automated Learning, Data Mining, Financial Engineering, and Intelligent Agents
Finding Temporal Relations: Causal Bayesian Networks vs. C4.5
ISMIS '00 Proceedings of the 12th International Symposium on Foundations of Intelligent Systems
Seabreeze Prediction Using Bayesian Networks
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
AI '00 Proceedings of the 13th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence
RFCT: An Association-Based Causality Miner
AI '02 Proceedings of the 15th Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
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
Distinguishing causal and acausal temporal relations
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
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We introduce a method for finding temporal and atemporal relations in nominal, causal data. This method searches for relations among variables that characterize the behavior of a single system. Data are gathered from variables of the system, and used to discover relations among the variables. In general, such rules could be causal or acausal. We formally characterize the problem and introduce RFCT, a hybrid tool based on the C4.5 classification software. By performing appropriate preprocessing and postprocessing, RFCT extends C4.5's domain of applicability to the unsupervised discovery of temporal relations among temporally ordered nominal data.