Data Mining for Decision Support

Supporting marketing decisions through subgroup discovery
  • Bojan Cestnik
  • Nada Lavrač
  • Peter Flach
  • Dragan Gamberger
  • Mihael Kline
Part of the The Springer International Series in Engineering and Computer Science book series (SECS, volume 745)

Abstract

This chapter presents two methods that combine data mining and decision support techniques with the aim to generate actionable knowledge. Both methods follow the same methodology in which data mining is used to support decision-making. The methodology consists of the following phases: business understanding; data acquisition, data understanding and preprocessing; data mining through subgroup discovery; subgroup evaluation; and deployment for decision support. The two methods have been applied to support decisionmaking in marketing.

Keywords

Fatigue Marketing Expense Hull Product Line 

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References

  1. Berger, J. O. (1985). Statistical Decision Theory and Bayesian Analysis, Springer.MATHGoogle Scholar
  2. Berry, M. J. A. and Linoff, G. S. (2000). Mastering Data Mining, The Art and Science of Customer Relationship Management, Wiley.Google Scholar
  3. Cestnik, B., Lavrač, N., Železný, F., Gamberger, D., Todorovski, L. and Kline, M. (2002). Data mining for decision support in marketing: A case study in targeting a marketing campaign. Proc. ECML/PKDD-2002 Workshop on Integration and Collaboration Aspects of Data Mining, Decision Support and Meta-Learning, IDDM-2002. (eds. Bohanec, M., Kavšek, B., Lavrač, N. and Mladenić, D.), Helsinki, Finland, 25–34.Google Scholar
  4. Clark, P. and Boswell, R. (1991). Rule induction with CN2: Some recent improvements. Proc. Fifth European Working Session on Learning. Springer, 151–163.Google Scholar
  5. Clark, P. and Niblett, T. (1989). The CN2 induction algorithm, Machine Learning, Vol. 3, No. 4, 261–283.Google Scholar
  6. Flach, P. and Gamberger, D. (2001). Subgroup evaluation and decision support for a direct mailing marketing problem. Proc. ECML/PKDD-2001 Workshop Integrating Aspects of Data Mining, Decision Support and Meta-Learning (IDDM-2001). (eds. Giraud-Carrier, C., Lavrač, N., Moyle, S. A. and Kavšek, B.), Freiburg, Germany, 45–56.Google Scholar
  7. Gamberger, D. and Lavrač, N. (2002). Expert-guided subgroup discovery: Methodology and application, Journal of Artificial Intelligence Research, Vol. 17, 501–527.MATHGoogle Scholar
  8. Kotler, P. (1991). Marketing Management: Analysis, Planning and Control, Prentice-Hall.Google Scholar
  9. Lavrač, N., Flach, P., Kavšek, B. and Todorovski, L. (2002a). Adapting classification rule induction to subgroup discovery. Proc. 2002 IEEE International Conference on Data Mining. IEEE Press, 266–273.Google Scholar
  10. Lavrač, N., Železný, F. and Flach, P. (2002b). RSD: Relational subgroup discovery through first-order feature construction. Proc. Twelfth International Conferences on Inductive Logic Programming (ILP’02). Springer, 152–169.Google Scholar
  11. McDonald, M. and Dunbar, I. (2000). Using structured processes and systems to help managers develop strategic segmentation, Journal of Targeting, Measurement and Analysis for Marketing, Vol. 9, No. 2, 109–127.CrossRefGoogle Scholar
  12. Mladenić, D. and Lavrač, N. (eds.), (2003). Data Mining and Decision Support for Business Competitiveness: A European Virtual Enterprise, Final Report, http://soleunet.ijs.si.Google Scholar
  13. Myers, J. H. (1996). Segmentation and Positioning for Strategic Marketing Decisions, American Marketing Association.Google Scholar
  14. Provost, F. and Fawcett, T. (2001). Robust classification for imprecise environments, Machine Learning, Vol. 42, No. 3, 203–231.MATHCrossRefGoogle Scholar
  15. Tynan, A. C. and Drayton, J. (1987). Market segmentation, Journal of Marketing Management, Vol. 2, No. 3, 301–335.CrossRefGoogle Scholar
  16. Wrobel, S. (1997). An algorithm for multi-relational discovery of subgroups. Proc. First European Symposium on Principles of Data Mining and Knowledge Discovery. Springer, 78–87.Google Scholar

Copyright information

© Springer Science+Business Media New York 2003

Authors and Affiliations

  • Bojan Cestnik
  • Nada Lavrač
  • Peter Flach
  • Dragan Gamberger
  • Mihael Kline

There are no affiliations available

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