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Does Relevance Matter to Data Mining Research?

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Data Mining: Foundations and Practice

Part of the book series: Studies in Computational Intelligence ((SCI,volume 118))

Summary

Data mining (DM) and knowledge discovery are intelligent tools that help to accumulate and process data and make use of it. We review several existing frameworks for DM research that originate from different paradigms. These DM frameworks mainly address various DM algorithms for the different steps of the DM process. Recent research has shown that many real-world problems require integration of several DM algorithms from different paradigms in order to produce a better solution elevating the importance of practice-oriented aspects also in DM research. In this chapter we strongly emphasize that DM research should also take into account the relevance of research, not only the rigor of it. Under relevance of research in general, we understand how good this research is in terms of the utility of its results. This chapter motivates development of such a new framework for DM research that would explicitly include the concept of relevance. We introduce the basic idea behind such framework and propose one sketch for the new framework for DM research based on results achieved in the information systems area having some tradition related to the relevance aspects of research.

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Author information

Authors and Affiliations

  1. Information Systems Group, Department of Computer Science, Eindhoven University of Technology, P.O. Box 513, 5600, MB Eindhoven, The Netherlands

    Mykola Pechenizkiy

  2. Department of Computer Science and Information Systems, University of Jyväskylä, P.O. Box 35, FIN-40351, Jyväskylä, Finland

    Mykola Pechenizkiy & Seppo Puuronen

  3. Department of Computer Science, Trinity College Dublin, Dublin 2, Ireland

    Alexey Tsymbal

  4. Corporate Technology Division, Siemens AG, Günther-Scharowsky-Str. 1, 91058, Erlangen, Germany

    Alexey Tsymbal

Authors
  1. Mykola Pechenizkiy
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  2. Seppo Puuronen
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  3. Alexey Tsymbal
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Editor information

Editors and Affiliations

  1. Department of Computer Science, San Jose State University, San Jose, CA, 95192, USA

    Tsau Young Lin

  2. Department of Computer Science and Information Systems, Kennesaw State University, Building 11, Room 3060 1000 Chastain Road, Kennesaw, GA, 30144, USA

    Ying Xie

  3. Department of Computer Science, The University at Stony Brook, Stony Brook, New York, 11794-4400, USA

    Anita Wasilewska

  4. Institute of Information Science, Academia Sinica, No 128, Academia Road, Section 2 Nankang, Taipei, 11529, Taiwan

    Churn-Jung Liau

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Pechenizkiy, M., Puuronen, S., Tsymbal, A. (2008). Does Relevance Matter to Data Mining Research?. In: Lin, T.Y., Xie, Y., Wasilewska, A., Liau, CJ. (eds) Data Mining: Foundations and Practice. Studies in Computational Intelligence, vol 118. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78488-3_15

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  • DOI: https://doi.org/10.1007/978-3-540-78488-3_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-78487-6

  • Online ISBN: 978-3-540-78488-3

  • eBook Packages: EngineeringEngineering (R0)

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