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A Comparison of Mining Incomplete and Inconsistent Data

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Information and Software Technologies (ICIST 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 639))

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Abstract

We present experimental results on a comparison of incompleteness and inconsistency. Our experiments were conducted on 141 data sets, including 71 incomplete data and 62 inconsistent, created from eight original numerical data sets. We used the Modified Learning from Examples Module version 2 (MLEM2) rule induction algorithm for data mining. Among eight types of data sets combined with three kinds of probabilistic approximations used in experiments, in 12 out of 24 combinations the error rate, computed as a result of ten-fold cross validation, was smaller for inconsistent data (two-tailed test, 5 % significance level). For one data set, combined with all three probabilistic approximations, the error rate was smaller for incomplete data. For remaining nine combinations the difference in performance was statistically insignificant. Thus, we may claim that there is some experimental evidence that incompleteness is generally worse than inconsistency for data mining.

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS, 66045, USA

    Patrick G. Clark, Cheng Gao & Jerzy W. Grzymala-Busse

  2. Department of Expert Systems and Artificial Intelligence, University of Information Technology and Management, 35-225, Rzeszow, Poland

    Jerzy W. Grzymala-Busse

Authors
  1. Patrick G. Clark
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  2. Cheng Gao
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  3. Jerzy W. Grzymala-Busse
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Corresponding author

Correspondence to Jerzy W. Grzymala-Busse .

Editor information

Editors and Affiliations

  1. Kaunas University of Technology , Kaunas, Lithuania

    Giedre Dregvaite

  2. Kaunas University of Technology , Kaunas, Lithuania

    Robertas Damasevicius

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© 2016 Springer International Publishing Switzerland

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Clark, P.G., Gao, C., Grzymala-Busse, J.W. (2016). A Comparison of Mining Incomplete and Inconsistent Data. In: Dregvaite, G., Damasevicius, R. (eds) Information and Software Technologies. ICIST 2016. Communications in Computer and Information Science, vol 639. Springer, Cham. https://doi.org/10.1007/978-3-319-46254-7_33

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  • DOI: https://doi.org/10.1007/978-3-319-46254-7_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46253-0

  • Online ISBN: 978-3-319-46254-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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