A normalization method for contextual data: Experience from a large-scale application

  • Sylvain Létourneaue
  • Stan Matwin
  • Fazel Familie
Regular Papers Applications of ML
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1398)


This paper describes a pre-processing technique to normalize contextually-dependent data before applying Machine Learning algorithms. Unlike many previous methods, our approach to normalization does not assume that the learning task is a classification task. We propose a data pre-processing algorithm which modifies the relevant attributes so that the effects of the contextual attributes on the relevant attributes are cancelled. These effects are modeled using a novel approach, based on the analysis of variance of the contextual attributes. The method is applied on a massive data repository in the area of aircraft maintenance.


Learning in contextual domains attribute normalization datamining 


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Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Sylvain Létourneaue
    • 1
  • Stan Matwin
    • 2
  • Fazel Familie
    • 1
  1. 1.Integrated Reasoning GroupNational Research Council of CanadaOttawa
  2. 2.School of Information Technology and EngineeringUniversity of OttawaCanada

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