Does Relevance Matter to Data Mining Research? - Data Mining: Foundations and Practice - page 245

Does Relevance Matter to Data Mining
Research?

Mykola Pechenizkiy 1 , 2 , Seppo Puuronen 2 , and Alexey Tsymbal 3 , 4

1

Information Systems Group, Department of Computer Science, Eindhoven
University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands
[e-mail]

2

Department of Computer Science and Information Systems,
University of Jyvaskyla, P.O. Box 35, FIN-40351, Jyvaskyla, Finland
[e-mail],[e-mail]

3

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

4

Corporate Technology Division, Siemens AG, Gunther-Scharowsky-Str. 1,
91058 Erlangen, Germany
[e-mail]

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.

1 Introduction

Data mining (DM) and knowledge discovery are intelligent tools that help
to accumulate and process data and make use of it [13]. DM bridges many
technical areas, including databases, statistics, machine learning, and human-
computer interaction. The set of DM processes used to extract and verify
patterns in data is the core of the knowledge discovery process [40]. These
processes include data cleaning, feature transformation, algorithm and para-
meter selection, and evaluation, interpretation and validation (Fig. 1).
M. Pechenizkiy et al.: Does Relevance Matter to Data Mining Research? , Studies in Computa-