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.
|Title of host publication||Data Mining: Foundations and Practice|
|Editors||T.Y. Lin, Y. Xie, A. Wasilewska, C.J. Liau|
|Place of Publication||Berlin|
|Publication status||Published - 2008|
|Name||Studies in Computational Intelligence|