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Foundations and Novel Approaches in Data Mining pp 155–167Cite as

Mining for Patterns Based on Contingency Tables by KL-Miner – First Experience

Part of the Studies in Computational Intelligence book series (SCI,volume 9)

Abstract

A new datamining procedure called KL-Miner is presented. The procedure mines for various patterns based on evaluation of two–dimensional contingency tables, including patterns of statistical or information theoretic nature. The procedure is aresult of continued development of the academic system LISp-Miner for KDD.

Keywords

  • Contingency Table
  • Association Rule
  • Data Matrix
  • Partial Condition
  • Relevant Condition

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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  • DOI: 10.1007/11539827_9
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Author information

Authors and Affiliations

  1. EuroMISE Centrum – Cardio, USA

    Jan Rauch

  2. Department of Information Technology, Italy

    Milan šimůnek

  3. Department of Information and Knowledge Engineering, Czech Republic

    Jan Rauch & Václav Lín

  4. University of Economics, Laboratory for Intelligent Systems, Prague, nám. W. Churchilla 4, 130 67 Praha 3,, Czech Republic

    Milan šimůnek

Authors
  1. Jan Rauch
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  2. Milan šimůnek
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  3. Václav Lín
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    Rauch, J., šimůnek, M., Lín, V. Mining for Patterns Based on Contingency Tables by KL-Miner – First Experience. In: Young Lin, T., Ohsuga, S., Liau, CJ., Hu, X. (eds) Foundations and Novel Approaches in Data Mining. Studies in Computational Intelligence, vol 9. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539827_9

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    • DOI: https://doi.org/10.1007/11539827_9

    • Published:

    • Publisher Name: Springer, Berlin, Heidelberg

    • Print ISBN: 978-3-540-28315-7

    • Online ISBN: 978-3-540-31229-1

    • eBook Packages: EngineeringEngineering (R0)