Data Mining for Decision Support
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.
KeywordsFatigue Marketing Expense Hull Product Line
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- Berry, M. J. A. and Linoff, G. S. (2000). Mastering Data Mining, The Art and Science of Customer Relationship Management, Wiley.Google Scholar
- 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
- 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
- Clark, P. and Niblett, T. (1989). The CN2 induction algorithm, Machine Learning, Vol. 3, No. 4, 261–283.Google Scholar
- 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
- Kotler, P. (1991). Marketing Management: Analysis, Planning and Control, Prentice-Hall.Google Scholar
- 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
- 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
- Myers, J. H. (1996). Segmentation and Positioning for Strategic Marketing Decisions, American Marketing Association.Google Scholar
- 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