Abstract
The use of unsupervised fuzzy learning methods produces a large number of alternative classifications. This paper presents and analyzes a series of criteria to select the most suitable of these classifications. Segmenting the clients’ portfolio is important in terms of decision-making in marketing because it allows for the discovery of hidden profiles which would not be detected with other methods and it establishes different strategies for each defined segment. In the case included, classifications have been obtained via the LAMDA algorithm. The use of these criteria reduces remarkably the search space and offers a tool to marketing experts in their decision-making.
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Sánchez, G., Agell, N., Aguado, J.C., Sánchez, M., Prats, F. (2007). Selection Criteria for Fuzzy Unsupervised Learning: Applied to Market Segmentation. In: Melin, P., Castillo, O., Aguilar, L.T., Kacprzyk, J., Pedrycz, W. (eds) Foundations of Fuzzy Logic and Soft Computing. IFSA 2007. Lecture Notes in Computer Science(), vol 4529. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72950-1_31
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DOI: https://doi.org/10.1007/978-3-540-72950-1_31
Publisher Name: Springer, Berlin, Heidelberg
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