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Complexity of Rule Sets Induced by Characteristic Sets and Generalized Maximal Consistent Blocks

  • Patrick G. Clark
  • Cheng Gao
  • Jerzy W. Grzymala-BusseEmail author
  • Teresa Mroczek
  • Rafal Niemiec
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10842)

Abstract

We study mining incomplete data sets with two interpretations of missing attribute values, lost values and “do not care” conditions. For data mining we use characteristic sets and generalized maximal consistent blocks. Additionally, we use three types of probabilistic approximations, lower, middle and upper, so altogether we apply six approaches to data mining. Since it was shown that an error rate, associated with such data mining is not universally smaller for any approach, we decided to compare complexity of induced rule sets. Therefore, our objective is to compare six approaches to mining incomplete data sets in terms of complexity of induced rule sets. We conclude that there are statistically significant differences between these approaches.

Keywords

Incomplete data Lost values “do not care” conditions Characteristic sets Maximal consistent blocks MLEM2 rule induction algorithm Probabilistic approximations 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Patrick G. Clark
    • 1
  • Cheng Gao
    • 1
  • Jerzy W. Grzymala-Busse
    • 1
    • 2
    Email author
  • Teresa Mroczek
    • 2
  • Rafal Niemiec
    • 2
  1. 1.Department of Electrical Engineering and Computer ScienceUniversity of KansasLawrenceUSA
  2. 2.Department of Expert Systems and Artificial IntelligenceUniversity of Information Technology and ManagementRzeszowPoland

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