Title:
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On the hybridation of artificial intelligence and statistics for effective knowledge discovery in ill-structured domains with messy data
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Author:
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Gibert, Karina; Rodríguez Silva, Gustavo; García Rudolph, A.
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Other authors:
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Universitat Politècnica de Catalunya. Departament d'Estadística i Investigació Operativa; Universitat Politècnica de Catalunya. KEMLG - Grup d'Enginyeria del Coneixement i Aprenentatge Automàtic |
Abstract:
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Several experiencies highlighted the suitability of combinining AI techniques with
Clustering techniques for effective KDD in very complex domains. In this work the benefits of using hybrid methodologies for extracting novel, valid, useful and ultimately understandable knowledge from very complex phenomenons is presented. The importance of including prior expert
knowledge as a semmantic biass of the clusters discovery is analyzed as well as the added value of providing interpretation-support tools for assisting both expert and user in the final generation of understandable and explicit knowledge. This approach has shown successful results in some
real applications from very different domains. Here results on environmental systems, particularly waste water treatment plants as well as medical domains, spinal cord lesion are presented. We can conclude than classical techniques perform poorly in front of very complex realities, where either
algebraic an logics structures have to be modeled to fully explain the domain behaviour. The multidisciplinar approach of designing hybrid methodologies provides very powerful tools to approach those kind of domains. |
Subject(s):
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-Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística aplicada::Qualitat total -Artificial intelligence -Statistics -Sewage disposal plants -Spinal Cord Injuries -Data mining -Intel·ligència artificial -Estadística -Aigua -- Depuració -Medul·la espinal -- Ferides i lesions -Mineria de dades |
Rights:
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Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
Document type:
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Article - Published version Conference Object |
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