Skip to main navigation Skip to search Skip to main content

Selection criteria for fuzzy unsupervised learning: Applied to market segmentation

  • Germán Sánchez*
  • , N. Agell
  • , Juan Carlos Aguado
  • , Mónica Sánchez
  • , Francesc Prats
  • *Corresponding author for this work

Research output: Book chapterConference contributionpeer-review

2 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationFoundations of Fuzzy Logic and Soft Computing - 12th International Fuzzy Systems Association World Congress, IFSA 2007, Proceedings
PublisherSpringer Verlag
Pages307-317
Number of pages11
ISBN (Print)9783540729174
DOIs
Publication statusPublished - 2007
Externally publishedYes
Event12th International Fuzzy Systems Association World Congress, IFSA 2007 - Cancun, Mexico
Duration: 18 Jun 200721 Jun 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4529 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference12th International Fuzzy Systems Association World Congress, IFSA 2007
Country/TerritoryMexico
CityCancun
Period18/06/0721/06/07

Keywords

  • Criteria for classification selection
  • Fuzzy unsupervised learning
  • Marketing applications

Fingerprint

Dive into the research topics of 'Selection criteria for fuzzy unsupervised learning: Applied to market segmentation'. Together they form a unique fingerprint.

Cite this