A methodology for dynamic data mining based on fuzzy clustering

Author	dc.contributor.author	Crespo, Fernando
Author	dc.contributor.author	Weber, Richard	es_CL
Admission date	dc.date.accessioned	2007-05-18T14:09:14Z
Available date	dc.date.available	2007-05-18T14:09:14Z
Publication date	dc.date.issued	2005-03-01
Cita de ítem	dc.identifier.citation	FUZZY SETS AND SYSTEMS 150 (2): 267-284 MAR 1 2005	en
Identifier	dc.identifier.issn	0165-0114
Identifier	dc.identifier.uri	https://repositorio.uchile.cl/handle/2250/124593
Abstract	dc.description.abstract	Dynamic data mining is increasingly attracting attention from the respective research community. On the other hand, users of installed data mining systems are also interested in the related techniques and will be even more since most of these installations will need to be updated in the future. For each data mining technique used, we need different methodologies for dynamic data mining. In this paper, we present a methodology for dynamic data mining based on fuzzy clustering. Using the implementation of the proposed system we show its benefits in two application areas: customer segmentation and traffic management.	en
Lenguage	dc.language.iso	en	en
Publisher	dc.publisher	ELSEVIER SCIENCE BV	en
Keywords	dc.subject	dynamic data mining	en
Título	dc.title	A methodology for dynamic data mining based on fuzzy clustering	en
Document type	dc.type	Artículo de revista