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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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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

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28315-7

  • Online ISBN: 978-3-540-31229-1

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