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A Comparison of Characteristic Sets and Generalized Maximal Consistent Blocks in Mining Incomplete Data

  • Patrick G. Clark
  • Cheng Gao
  • Jerzy W. Grzymala-BusseEmail author
  • Teresa Mroczek
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 854)

Abstract

We discuss two interpretations of missing attribute values, lost values and “do not care” conditions. Both interpretations may be used for data mining based on characteristic sets. On the other hand, maximal consistent blocks were originally defined for incomplete data sets with “do not care” conditions, using only lower and upper approximations. We extended definitions of maximal consistent blocks to both interpretations while using probabilistic approximations, a generalization of lower and upper approximations. Our main objective is to compare approximations based on characteristic sets with approximations based on maximal consistent blocks in terms of an error rate.

Keywords

Incomplete data mining Characteristic sets Maximal consistent blocks Rough set theory 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
  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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