Abstract
We present the Temporal Investigation Method for Enregistered Record Sequences II (TIMERS II), which can be used to classify the relationship between a decision attribute and a number of condition attributes as instantaneous, causal, or acausal. In this paper we consider it possible to refer to both previous and next values of attributes in temporal rules, and thus enhance the definition of acausality. We also present a new algorithm for distinguishing between causality and acausality.
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References
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Hamilton, H.J., Karimi, K. (2005). The TIMERS II Algorithm for the Discovery of Causality. In: Ho, T.B., Cheung, D., Liu, H. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2005. Lecture Notes in Computer Science(), vol 3518. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11430919_86
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DOI: https://doi.org/10.1007/11430919_86
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26076-9
Online ISBN: 978-3-540-31935-1
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