C4.5: programs for machine learning
C4.5: programs for machine learning
Finding patterns in time series: a dynamic programming approach
Advances in knowledge discovery and data mining
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Scaling up dynamic time warping for datamining applications
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering Temporal Rules from Temporally Ordered Data
IDEAL '02 Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning
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
TimeSleuth: A Tool for Discovering Causal and Temporal Rules
ICTAI '02 Proceedings of the 14th IEEE International Conference on Tools with 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
Using dependence diagrams to summarize decision rule sets
Canadian AI'08 Proceedings of the Canadian Society for computational studies of intelligence, 21st conference on Advances in artificial intelligence
Learning bayesian networks in semi-deterministic systems
AI'06 Proceedings of the 19th international conference on Advances in Artificial Intelligence: Canadian Society for Computational Studies of Intelligence
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We describe TimeSleuth, a hybrid tool based on the C4.5 classification software, which is intended for the discovery of temporal/causal rules. Temporally ordered data are gathered from observable attributes of a system, and used to discover relations among the attributes. In general, such rules could be atemporal or temporal. We evaluate TimeSleuth using synthetic data sets with well-known causal relations as well as real weather data. We show that by performing appropriate preprocessing and postprocessing operations, TimeSleuth extends C4.5's domain of applicability to the unsupervised discovery of temporal relations among ordered data. We compare the results obtained from TimeSleuth to those of TETRAD and CaMML, and show that TimeSleuth performs better than the other systems.