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Detecting Groups of Anomalously Similar Objects in Large Data Sets

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Advances in Intelligent Data Analysis VI (IDA 2005)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 3646))

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Abstract

Pattern discovery is a facet of data mining concerned with the detection of ”small local” structures in large data sets. In high dimensions this is typically difficult because of the computational work involved in searching over the data space. In this paper we outline a tool called PEAKER which can detect patterns efficiently in high dimensions. We approach the subject through the two aspects of pattern discovery, detection and verification. We demonstrate various ways of using PEAKER as well as its various inherent properties, emphasizing the exploratory nature of the tool.

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Author information

Authors and Affiliations

  1. Department of Mathematics, Imperial College London, 180 Queens Gate, Huxley Building, London, SW7 2AZ, UK

    Zhicheng Zhang & David J. Hand

Authors
  1. Zhicheng Zhang
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  2. David J. Hand
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Editor information

Editors and Affiliations

  1. Institute for Information Technology, National Research Council Canada, Ottawa, Canada

    A. Fazel Famili

  2. LIACS, Leiden University, The Netherlands

    Joost N. Kok

  3. IFM, Linköping University, SE-58183, Linköping, Sweden

    José M. Peña

  4. Department of Computer Science, Universiteit Utrecht,  

    Arno Siebes

  5. Utrecht University, TB Utrecht,, P.O. box 80 089, NL-3508, the Netherlands

    Ad Feelders

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© 2005 Springer-Verlag Berlin Heidelberg

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Zhang, Z., Hand, D.J. (2005). Detecting Groups of Anomalously Similar Objects in Large Data Sets. In: Famili, A.F., Kok, J.N., Peña, J.M., Siebes, A., Feelders, A. (eds) Advances in Intelligent Data Analysis VI. IDA 2005. Lecture Notes in Computer Science, vol 3646. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552253_46

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  • DOI: https://doi.org/10.1007/11552253_46

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28795-7

  • Online ISBN: 978-3-540-31926-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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