A3 Book section, Chapters in research books
Does Relevance Matter to Data Mining Research? (2008)

Pechenizkiy, M., Puuronen, S., & Tsymbal, A. (2008). Does Relevance Matter to Data Mining Research?. In T. Y. Lin, Y. Xie, A. Wasilewska, & C.-J. Liau (Eds.), Data Mining : Foundations and Practice (pp. 251-275). Springer. Studies in computational intelligence, 118. https://doi.org/10.1007/978-3-540-78488-3_15

JYU authors or editors

Publication details

All authors or editors: Pechenizkiy, Mykola; Puuronen, Seppo; Tsymbal, Alexey

Parent publication: Data Mining : Foundations and Practice

Parent publication editors: Lin, Tsau Young; Xie, Ying; Wasilewska, Anita; Liau, Churn-Jung

ISBN: 978-3-540-78487-6

eISBN: 978-3-540-78488-3

Journal or series: Studies in computational intelligence

ISSN: 1860-949X

eISSN: 1860-9503

Publication year: 2008

Number in series: 118

Pages range: 251-275

Number of pages in the book: 562

Publisher: Springer

Place of Publication: Berlin

Publication country: Germany

Publication language: English

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

Publication open access: Not open

Publication channel open access: 

Abstract

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.

Keywords: information retrieval; data mining; algorithms

Free keywords: Association Rule; Data Mining Algorithm; Granular Computing; Data Mining Task; Data Mining Process

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Ministry reporting: Yes

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