Multiobjective Evolutionary Induction of Subgroup Discovery Fuzzy Rules: A Case Study in Marketing
Identificadores
URI: https://hdl.handle.net/10481/77889Metadatos
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Springer
Materia
Data mining Descriptive induction Multiobjective evolutionary algorithms Genetic fuzzy systems Subgroup discovery
Fecha
2006Referencia bibliográfica
Published version: Berlanga, F... [et al.] (2006). Multiobjective Evolutionary Induction of Subgroup Discovery Fuzzy Rules: A Case Study in Marketing. In: Perner, P. (eds) Advances in Data Mining. Applications in Medicine, Web Mining, Marketing, Image and Signal Mining. ICDM 2006. Lecture Notes in Computer Science(), vol 4065. Springer, Berlin, Heidelberg. [https://doi.org/10.1007/11790853_27]
Patrocinador
Spanish Ministry of Science and Technology; FEDER TIC-2005-08386-C05-01 and TIC-2005- 08386-C05-03; TIN2004-20061-E and TIN2004-21343-EResumen
This paper presents a multiobjective genetic algorithm which obtains
fuzzy rules for subgroup discovery in disjunctive normal form. This kind of
fuzzy rules lets us represent knowledge about patterns of interest in an
explanatory and understandable form which can be used by the expert. The
evolutionary algorithm follows a multiobjective approach in order to optimize
in a suitable way the different quality measures used in this kind of problems.
Experimental evaluation of the algorithm, applying it to a market problem
studied in the University of Mondragón (Spain), shows the validity of the
proposal. The application of the proposal to this problem allows us to obtain
novel and valuable knowledge for the experts.