Título:
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A Methodology of knowledge discovery in serial measurement applied to a psychiatric domain
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Autor/a:
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Rodas Osollo, Jorge Enrique; Gibert, Karina; Rojo, Emilio; Cortés García, Claudio Ulises
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Otros autores:
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Universitat Politècnica de Catalunya. Departament d'Estadística i Investigació Operativa; Universitat Politècnica de Catalunya. Departament de Ciències de la Computació; Universitat Politècnica de Catalunya. KEMLG - Grup d'Enginyeria del Coneixement i Aprenentatge Automàtic |
Abstract:
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The 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. |
Materia(s):
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-Àrees temàtiques de la UPC::Informàtica::Sistemes d'informació -Àrees temàtiques de la UPC::Ciències de la salut -Knowledge discovery -Serial measurement -KDSM -Psychiatry -Classification -Clustering -Serial Measurement over time -Ill-structured domains |
Derechos:
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Tipo de documento:
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Artículo - Versión publicada Informe |
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