Agrupamento de dados semissupervisionado na geração de regras fuzzy

• Ano de defesa: 2010
• Autor(a) principal: Lopes, Priscilla de Abreu
• Orientador(a): Camargo, Heloisa de Arruda
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de São Carlos; Câmpus São Carlos
• Programa de Pós-Graduação: Programa de Pós-Graduação em Ciência da Computação - PPGCC
• Palavras-chave em Português: Aprendizado Semi-Supervisionado; Agrupamento Fuzzy de Dados; Geração de Regras Fuzzy
• Palavras-chave em Inglês: Semi-Supervised Learning; Fuzzy Data Clustering; Fuzzy Rules Generation
• Área do conhecimento CNPq: CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::METODOLOGIA E TECNICAS DA COMPUTACAO
• Link de acesso: https://repositorio.ufscar.br/handle/ufscar/7061

Inductive learning is, traditionally, categorized as supervised and unsupervised. In supervised learning, the learning method is given a labeled data set (classes of data are known). Those data sets are adequate for problems of classification and regression. In unsupervised learning, unlabeled data are analyzed in order to identify structures embedded in data sets. Typically, clustering methods do not make use of previous knowledge, such as classes labels, to execute their job. The characteristics of recently acquired data sets, great volume and mixed attribute structures, contribute to research on better solutions for machine learning jobs. The proposed research fits into this context. It is about semi-supervised fuzzy clustering applied to the generation of sets of fuzzy rules. Semi-supervised clustering does its job by embodying some previous knowledge about the data set. The clustering results are, then, useful for labeling the remaining unlabeled data in the set. Following that, come to action the supervised learning algorithms aimed at generating fuzzy rules. This document contains theoretic concepts, that will help in understanding the research proposal, and a discussion about the context wherein is the proposal. Some experiments were set up to show that this may be an interesting solution for machine learning jobs that have encountered difficulties due to lack of available information about data.