• JoomlaWorks Simple Image Rotator
  • JoomlaWorks Simple Image Rotator
  • JoomlaWorks Simple Image Rotator
  • JoomlaWorks Simple Image Rotator
  • JoomlaWorks Simple Image Rotator
  • JoomlaWorks Simple Image Rotator
  • JoomlaWorks Simple Image Rotator
  • JoomlaWorks Simple Image Rotator
  • JoomlaWorks Simple Image Rotator
  • JoomlaWorks Simple Image Rotator
 
  Bookmark and Share
 
 
Master's Dissertation
DOI
10.11606/D.55.2018.tde-05032018-164707
Document
Author
Full name
Cláudio Alex Jorge da Rocha
Institute/School/College
Knowledge Area
Date of Defense
Published
São Carlos, 1999
Supervisor
Committee
Rezende, Solange Oliveira (President)
Lôbo, Raysildo Barbosa
Monard, Maria Carolina
Title in Portuguese
Redes Bayesianas para Extração de Conhecimento de Bases de Dados, Considerando a Incorporação de Conhecimento de Fundo e o Tratamento de Dados Incompletos
Keywords in Portuguese
Não disponível
Abstract in Portuguese
O interesse cada vez maior das empresas em adquirir novas tecnologias de processarnento e armazenamento de dados, além de visualizar a informação como seu maior patrimônio, tem direcionado várias pesquisas para o estudo do processo de transformação desses dados em conhecimento, o que pode proporcionar um auxílio efetivamente inteligente à tomada de decisão. Nesse contexto, o processo de Extração de Conhecimento de Bases de Dados (KDD - Knowledge Discovery in Database) desponta como uma tecnologia capaz de cooperar amplamente na busca do conhecimento embutido nos dados. Essa busca pode ser realizada utilizando métodos estatísticos e/ou técnicas de Inteligência Artificial, especialmente as que manipulam incerteza, que são amplamente aplicados na análise de dados com objetivo de encontrar relações de interesse. As redes Bayesianss representam um dos modelos mais proeminentes para encontrar essas relações. Este trabalho envolve a investigação dos conceitos, técnicas, métodos e ferramentas Bayesianas para auxiliar o processo de extração de conhecimento de bases de dados, considerando a incorporação de conhecimento de fundo, bem como o tratamento de dados incompletos.
Title in English
Not available
Keywords in English
Not available
Abstract in English
The fa.ct that companies are becoming more and more interested in acquiring new technologies for processing and storing data, as well as the view that information is their largest asset, has led to much regearch about the process of transforming this data into knowledge, which can malce it possible to aid decision-making in an effective and intelligent miner. In this context, the Knowledge Discovery in Databases (KDD) proress has emerged as a technology well suited to searching for knowledge that is embedded in the data. This search can be made using statistical methods and/or Artificial Intelligence techniques, especially those that manipulate uncertainty, which are widely used to analyze data and find interesting relations. Bayesian networks represent one of the more proeminent models for finding these relations. This work involves the investigation of Bayesian concepts, techniques, methods and tools to aid the process of extracting knowledge from databases, considering the inclusion of background knowledge, as well as treatment of incomplete data.
 
WARNING - Viewing this document is conditioned on your acceptance of the following terms of use:
This document is only for private use for research and teaching activities. Reproduction for commercial use is forbidden. This rights cover the whole data about this document as well as its contents. Any uses or copies of this document in whole or in part must include the author's name.
Publishing Date
2018-03-05
 
WARNING: Learn what derived works are clicking here.
All rights of the thesis/dissertation are from the authors
CeTI-SC/STI
Digital Library of Theses and Dissertations of USP. Copyright © 2001-2021. All rights reserved.