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2007
Springer

Selection Criteria for Fuzzy Unsupervised Learning: Applied to Market Segmentation

8 years 11 months ago
Selection Criteria for Fuzzy Unsupervised Learning: Applied to Market Segmentation
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.
Germán Sánchez, Núria Agell,
Added 08 Jun 2010
Updated 08 Jun 2010
Type Conference
Year 2007
Where IFSA
Authors Germán Sánchez, Núria Agell, Juan Carlos Aguado, Mónica Sánchez, Francesc Prats
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