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Heuristics for Ranking the Interestingness of Discovered Knowledge

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Methodologies for Knowledge Discovery and Data Mining (PAKDD 1999)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1574))

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Abstract

We describe heuristics, based upon information theory and statistics, for ranking the interestingness of summaries generated from databases. The tuples in a summary are unique, and therefore, can be considered to be a population described by some probability distribution. The four interestingness measures presented here are based upon common measures of diversity of a population: variance, the Simpson index, and the Shannon index. Using each of the proposed measures, we assign a single real value to a summary that describes its interestingness. Our experimental results show that the ranks assigned by the four interestingness measures are highly correlated.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Regina, Regina, Saskatchewan, Canada, S4S 0A2

    Robert J. Hilderman & Howard J. Hamilton

Authors
  1. Robert J. Hilderman
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  2. Howard J. Hamilton
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Systems Engineering, Yamaguchi University, Tokiwa-Dai, 2557, Ube, 755, Japan

    Ning Zhong

  2. Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Lizhu Zhou

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© 1999 Springer-Verlag Berlin Heidelberg

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Hilderman, R.J., Hamilton, H.J. (1999). Heuristics for Ranking the Interestingness of Discovered Knowledge. In: Zhong, N., Zhou, L. (eds) Methodologies for Knowledge Discovery and Data Mining. PAKDD 1999. Lecture Notes in Computer Science(), vol 1574. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48912-6_28

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  • DOI: https://doi.org/10.1007/3-540-48912-6_28

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65866-5

  • Online ISBN: 978-3-540-48912-2

  • eBook Packages: Springer Book Archive

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