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Data Mining, Rough Sets and Granular Computing pp 216–231Cite as

Finding Unexpected Patterns in Data

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Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 95))

Abstract

Many pattern discovery methods in the KDD literature have the drawbacks of (1) discovering too many obvious or irrelevant patterns and (2) not using prior knowledge systematically. In this chapter we present an approach that addresses these drawbacks. In particular we present an approach to characterizing the unexpectedness of patterns based on prior background knowledge in the form of beliefs. Based on this characterization of unexpectedness we present an algorithm, ZoomUR, for discovering unexpected patterns in data.

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Author information

Authors and Affiliations

  1. Operations and Information Management Department, The Wharton School, University of Pennsylvania, USA

    Balaji Padmanabhan

  2. Information Systems Department, Stern School of Business, New York University, USA

    Alexander Tuzhilin

Authors
  1. Balaji Padmanabhan
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  2. Alexander Tuzhilin
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Editor information

Editors and Affiliations

  1. Department of Mathematics and Computer Science, San Jose State University The Metropolitan University of Silicon Valley, One Washington Square, 95192-0103, San Jose, CA, USA

    Tsau Young Lin

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

    Yiyu Y. Yao

  3. Computer Science Division and Electronics Research Laboratory Department of Electrical and Electronics, University of California Berkeley Initiative in Soft Computing (BISC), 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

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© 2002 Springer-Verlag Berlin Heidelberg

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Padmanabhan, B., Tuzhilin, A. (2002). Finding Unexpected Patterns in Data. In: Lin, T.Y., Yao, Y.Y., Zadeh, L.A. (eds) Data Mining, Rough Sets and Granular Computing. Studies in Fuzziness and Soft Computing, vol 95. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1791-1_10

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  • DOI: https://doi.org/10.1007/978-3-7908-1791-1_10

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-7908-2508-4

  • Online ISBN: 978-3-7908-1791-1

  • eBook Packages: Springer Book Archive

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