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A Methodology of knowledge discovery in serial measurement applied to a psychiatric domain

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Rodas Osollo, Jorge Enrique
Gibert, KarinaMés informacióMés informacióMés informació
Rojo, Emilio
Cortés García, Claudio UlisesMés informacióMés informacióMés informació
Document typeResearch report
Defense date2001-11
Rights accessOpen Access
All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder
Abstract
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.
CitationRodas, J., Gibert, Karina, Rojo, E., Cortes, C. "A Methodology of knowledge discovery in serial measurement applied to a psychiatric domain". 2001. 
Is part ofLSI-01-53-R
URIhttp://hdl.handle.net/2117/97830
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  • Departament d'Estadística i Investigació Operativa - Reports de recerca [89]
  • KEMLG - Grup d'Enginyeria del Coneixement i Aprenentatge Automàtic - Reports de recerca [96]
  • Departament de Ciències de la Computació - Reports de recerca [1.106]
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