Abstract
The vagueness and uncertainty of data is a frequent problem in marketing research. Since rough sets have already proven their usefulness in dealing with such data in important domains like medicine and image processing, the question arises, whether they are a useful concept for marketing as well. Against this background we investigate the rough set theory-based LEM2 algorithm as a classification tool for marketing research. Its performance is demonstrated by means of synthetic as well as real-world marketing data. Our empirical results provide evidence that the LEM2 algorithm undoubtedly deserves more attention in marketing research as it is the case so far.
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References
BAZAN, J.G., SZCZUKA, M.S., WOJNA, A. and WOJNARSKI, M. (2004): On the Evolution of Rough Set Exploration System. In: S. Tsumoto, R. Slowinski, J. Komorowski, and J.W. Grzymala-Busse (Eds.): Rough Sets and Current Trends in Computing, Proceedings of the 4th International RSCTC’04 Conference. Springer, Berlin, 592–601.
GRZYMALA-BUSSE, J.W. (1997): A New Version of the Rule Induction System LERS. Fundamenta Informaticae, 31,1, 27–39.
GRZYMALA-BUSSE, J.W. (2003): MLEM2 — Discretization During Rule Induction. In: M.A. Klopotek, T.T. Wierzchon, and K. Trojanowski (Eds.): Intelligent Information Processing and Web Mining, Proceedings of the International IISPWM’03 Conference. Springer, Berlin, 499–508.
NGUYEN, S.H. (1999): Data Regularity Analysis and Applications in Data Mining. Ph.D. Thesis, Department of Mathematics, Computer Science and Mechanics, Warsaw University, Warsaw.
PAWLAK, Z. (1982): Rough Sets. International Journal of Computer and Information Sciences, 11,5, 341–356.
PAWLAK, Z. (1991): Rough Sets: Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht.
WOJNARSKI, M. (2003): LTF-C: Architecture, Training Algorithm and Applications of New Neural Classifier. Fundamenta Informaticae, 54,1, 89–105.
ZUPAN, B., BOHANEC, M. and DEMSAR, J. (1997): Machine Learning by Function Decomposition, In: D.H. Fisher (Ed.): Proceedings of the 14th International Conference on Machine Learning. Morgan Kaufmann Publishers, Nashville, 421–429.
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© 2007 Springer-Verlag Berlin Heidelberg
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Decker, R., Kroll, F. (2007). Classification in Marketing Research by Means of LEM2-generated Rules. In: Decker, R., Lenz, H.J. (eds) Advances in Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70981-7_48
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DOI: https://doi.org/10.1007/978-3-540-70981-7_48
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-70980-0
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