Selection Criteria for Fuzzy Unsupervised Learning: Applied to Market Segmentation

Authors:
Germán Sánchez;Núria Agell;Juan Carlos Aguado;Mónica Sánchez;Francesc Prats
Affiliations:
ESADE-URL. Avda. Pedralbes, 60. 08034 Barcelona,;ESADE-URL. Avda. Pedralbes, 60. 08034 Barcelona,;ESAII-UPC. Avda. del Canal Olímpic, s/n. 08860 Castelldefels,;MA2-UPC. Jordi Girona, 1-3 08034 Barcelona,;MA2-UPC. Jordi Girona, 1-3 08034 Barcelona,
Venue:
IFSA '07 Proceedings of the 12th international Fuzzy Systems Association world congress on Foundations of Fuzzy Logic and Soft Computing
Year:
2007

Citing 2
Cited 1

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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.