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Mining Non-redundant Information-Theoretic Dependencies between Itemsets

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Data Warehousing and Knowledge Discovery (DaWaK 2010)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 6263))

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Abstract

We present an information-theoretic framework for mining dependencies between itemsets in binary data. The problem of closure-based redundancy in this context is theoretically investigated, and we present both lossless and lossy pruning techniques. An efficient and scalable algorithm is proposed, which exploits the inclusion-exclusion principle for fast entropy computation. This algorithm is empirically evaluated through experiments on synthetic and real-world data.

An extended version of this paper is available as a technical report [1].

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Author information

Authors and Affiliations

  1. Dept. of Mathematics and Computer Science, University of Antwerp, Belgium

    Michael Mampaey

Authors
  1. Michael Mampaey
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Editor information

Editors and Affiliations

  1. Department of Computer Science, Aalborg University, Selma Lagerløfs Vej 300, 9220, Aalborg, Denmark

    Torben Bach Pedersen

  2. IBM India Research Lab, 4, Block C, Institutional Area, Vasant Kunj, 110 070, New Delhi, India

    Mukesh K. Mohania

  3. Institute of Software Technology, Vienna University of Technology, Favoritenstr. 9-11/188, 1040, Vienna, Austria

    A Min Tjoa

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Mampaey, M. (2010). Mining Non-redundant Information-Theoretic Dependencies between Itemsets. In: Bach Pedersen, T., Mohania, M.K., Tjoa, A.M. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2010. Lecture Notes in Computer Science, vol 6263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15105-7_11

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  • DOI: https://doi.org/10.1007/978-3-642-15105-7_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15104-0

  • Online ISBN: 978-3-642-15105-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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