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dc.contributor.authorRodas Osollo, Jorge Enrique
dc.contributor.authorGibert, Karina
dc.contributor.authorRojo, Emilio
dc.contributor.authorCortés García, Claudio Ulises
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Estadística i Investigació Operativa
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.date.accessioned2016-12-07T10:14:43Z
dc.date.available2016-12-07T10:14:43Z
dc.date.issued2001-11
dc.identifier.citationRodas, J., Gibert, Karina, Rojo, E., Cortes, C. "A Methodology of knowledge discovery in serial measurement applied to a psychiatric domain". 2001.
dc.identifier.urihttp://hdl.handle.net/2117/97830
dc.description.abstractThe paper introduces a methodology of Knowledge Discovery in Serial Measurement (KDSM) for analyzing repeated very short time series with a blocking factor in ill-structured domains. This proposal focuses on results obtained on a real application to psychiatry, where common statistical analysis (time series analysis, multivariate\dots) and artificial intelligence techniques (knowledge based methods, inductive learning) used independently are often inadequate because of the intrinsic characteristics of the domain. This work shows how the limitations of the classical approaches are overcomed by using KDSM. KDSM is built as the combination of {\it clustering based on rules}, introduced by Gibert (1994), with some Inductive Learning (AI) and clustering (Statistics) techniques.
dc.format.extent23 p.
dc.language.isoeng
dc.relation.ispartofseriesLSI-01-53-R
dc.subjectÀrees temàtiques de la UPC::Informàtica::Sistemes d'informació
dc.subjectÀrees temàtiques de la UPC::Ciències de la salut
dc.subject.otherKnowledge discovery
dc.subject.otherSerial measurement
dc.subject.otherKDSM
dc.subject.otherPsychiatry
dc.subject.otherClassification
dc.subject.otherClustering
dc.subject.otherSerial Measurement over time
dc.subject.otherIll-structured domains
dc.titleA Methodology of knowledge discovery in serial measurement applied to a psychiatric domain
dc.typeExternal research report
dc.contributor.groupUniversitat Politècnica de Catalunya. KEMLG - Grup d'Enginyeria del Coneixement i Aprenentatge Automàtic
dc.rights.accessOpen Access
local.identifier.drac646971
dc.description.versionPostprint (published version)
local.citation.authorRodas, J.; Gibert, Karina; Rojo, E.; Cortes, C.


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