Abstract
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.
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Karimi, K., Hamilton, H.J. (2003). Discovering Temporal/Causal Rules: A Comparison of Methods. In: Xiang, Y., Chaib-draa, B. (eds) Advances in Artificial Intelligence. Canadian AI 2003. Lecture Notes in Computer Science, vol 2671. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44886-1_15
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DOI: https://doi.org/10.1007/3-540-44886-1_15
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