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Master's Dissertation
DOI
10.11606/D.55.2018.tde-12032018-104623
Document
Author
Full name
Thereza Patrícia Pereira Padilha
Institute/School/College
Knowledge Area
Date of Defense
Published
São Carlos, 1999
Supervisor
Committee
Rezende, Solange Oliveira (President)
Carvalho, André Carlos Ponce de Leon Ferreira de
Castiñeira, Maria Inés
Title in Portuguese
Investigação de Algoritmos de Aprendizado de Máquina Pertencentes ao Paradigma Estatístico para Aquisição de Conhecimento
Keywords in Portuguese
Não disponível
Abstract in Portuguese
Uma grande revolução tecnológica ocorreu nos últimos anos em diversas áreas relacionadas a ciência da computação. Um dos aspectos que mais influenciou esta revolução foi o armazenamento, o processamento e a análise de grandes quantidades de dados geradas por várias empresas e centros de pesquisas. Com isso, a incorporação de métodos e técnicas estatísticas para a aquisição de conhecimento de dados na área de Aprendizado de Máquina tem apresentado um grande crescimento. O propósito desse trabalho é investigar alguns algoritmos de Aprendizado de Máquina pertencente ao paradigma estatístico para a aquisição de conhecimento a partir de conjuntos de dados. Nessa investigação foram estudados os algoritmos estatísticos Naive Bayes, Auto Class, Auto Class Pro e K-Means. Dois estudos de casos (um conjunto de plantas iris e um conjunto de domicílios de clientes) foram realizados verificando, entre outros, o comportamento desses algoritmos, a relevância dos atributos dos conjuntos de dados e apresentando os clusters encontrados nas ferramentas de visualização.
Title in English
Not available
Keywords in English
Not available
Abstract in English
A technological revolution has been happenning in the last few years in many areas related to computer science. One of the aspects that lias most influenced this revolution is the storage, processing and analysis of large quantities of data generated by vazious companies and research centers. All this has led to the incorporation of statistical methods and techniques for knowledge acquisition in the arca of Machine Learning has shown a large growth. The purpose of this work is to investigate some Machine Learning algorithms that belong to the statistical paradigm for knowledge acquisition from datasets. In this investigation, the statistical algorithms Naive Bayes, Auto Class, Auto Class Pro and K-Means were used. Two case studies (one with a set about iris plants and another with a set about client households) were raade to verify, among other things, the behavior of these algorithms and the relevance of the attributes in the datasets, and to present the clusters found, using visualization tools.
 
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Publishing Date
2018-03-12
 
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