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Evaluating the Correlation Between Objective Rule Interestingness Measures and Real Human Interest

  • Deborah R Carvalho
  • Alex A. Freitas
  • Nelson Ebecken
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3721)

Abstract

In the last few years, the data mining community has proposed a number of objective rule interestingness measures to select the most interesting rules, out of a large set of discovered rules. However, it should be recalled that objective measures are just an estimate of the true degree of interestingness of a rule to the user, the so-called real human interest. The latter is inherently subjective. Hence, it is not clear how effective, in practice, objective measures are. More precisely, the central question investigated in this paper is: “how effective objective rule interestingness measures are, in the sense of being a good estimate of the true, subjective degree of interestingness of a rule to the user?” This question is investigated by extensive experiments with 11 objective rule interestingness measures across eight real-world data sets.

Keywords

Rank Number True Degree Exception Rule Rule Interestingness Minimum Generalization 
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 2005

Authors and Affiliations

  • Deborah R Carvalho
    • 1
    • 3
  • Alex A. Freitas
    • 2
  • Nelson Ebecken
    • 3
  1. 1.Universidade Tuiuti do Paraná (UTP)Brazil
  2. 2.Computing Laboratory University of KentUK
  3. 3.COPPE/ Universidade Federal do Rio de JaneiroBrazil

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