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Discovering Temporal Rules from Temporally Ordered Data

  • Kamran Karimi
  • Howard J. Hamilton
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2412)

Abstract

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.

Keywords

Decision Rule Bayesian Network Temporal Order Temporal Relation Decision Attribute 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Kamran Karimi
    • 1
  • Howard J. Hamilton
    • 1
  1. 1.Department of Computer ScienceUniversity of ReginaReginaCanada

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