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Separating Structure from Interestingness

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Advances in Knowledge Discovery and Data Mining (PAKDD 2004)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3056))

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Abstract

Condensed representations of pattern collections have been recognized to be important building blocks of inductive databases, a promising theoretical framework for data mining, and recently they have been studied actively. However, there has not been much research on how condensed representations should actually be represented.

In this paper we propose a general approach to build condensed representations of pattern collections. The approach is based on separating the structure of the pattern collection from the interestingness values of the patterns. We study also the concrete case of representing the frequent sets and their (approximate) frequencies following this approach: we discuss the trade-offs in representing the frequent sets by the maximal frequent sets, the minimal infrequent sets and their combinations, and investigate the problem approximating the frequencies from samples by giving new upper bounds on sample complexity based on frequent closed sets and describing how convex optimization can be used to improve and score the obtained samples.

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Author information

Authors and Affiliations

  1. HIIT Basic Research Unit, Department of Computer Science, University of Helsinki, Finland

    Taneli Mielikäinen

Authors
  1. Taneli Mielikäinen
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Editor information

Editors and Affiliations

  1. School of Engineering and Information Technology, Deakin University, VIC 3125, Australia

    Honghua Dai

  2. University of Illinois at Urbana-Champaign, 61801, Urbana, IL, USA

    Ramakrishnan Srikant

  3. Faculty of Engineering and Information Technology, Centre for Quantum Computation and Intelligent Systems, and Australian ACS National Committee for Artificial Intelligence, University of Technology, Sydney, Australia

    Chengqi Zhang

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Mielikäinen, T. (2004). Separating Structure from Interestingness. In: Dai, H., Srikant, R., Zhang, C. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2004. Lecture Notes in Computer Science(), vol 3056. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24775-3_58

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  • DOI: https://doi.org/10.1007/978-3-540-24775-3_58

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22064-0

  • Online ISBN: 978-3-540-24775-3

  • eBook Packages: Springer Book Archive

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