Affiliations: [a] Department of Computer Science, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands | [b] Department of Computer Science and Information Systems, University of Jyväskylä, P.O. Box 35, Jyväskylä 40351, Finland | [c] Siemens AG, CT SE SCR 2, Erlangen, Germany | [x] Institute of Mathematics, Russian Academy of Science, Novosibirsk, 630090, Russia | [y] School of Computing and Mathematical Sciences, University of Greenwich, London SE10 9LS, UK
Abstract: Data mining (DM) research has successfully developed advanced DM techniques and algorithms over the last few decades, and many organisations have great expectations to take more benefit of their data warehouses in decision making. Currently, the strong focus of most DM-researchers is still only on technology-oriented topics. Commonly the DM research has several stakeholders, the major of which can be divided into internal and external ones each having their own point of view, and which are at least partly conflicting. The most important internal groups of stakeholders are the DM research community and academics in other disciplines. The most important external stakeholder groups are managers and domain experts who have their own utility-based interests to DM and DM research results. In this paper we discuss these practice-oriented points of view towards DM research and suggest broader discussions inside the DM research community about who should do that kind of research. We bring in the discussion several topics developed in the information systems (IS) discipline and show some similarities between IS and DM systems. DM systems have also their own peculiarities and we conclude that researchers who take into account human and organisational aspects related to DM systems need to have also some understanding about DM. This makes us suggest that the research area inside the DM community should be made broader than the current heavily technology-oriented one.
Keywords: Rigor vs. relevance in research, utility-based data mining, data mining stakeholders
