Agrupamento nebuloso de dados baseado em enxame de partículas: seleção por métodos evolutivos e combinação via relação nebulosa do tipo-2

• Ano de defesa: 2014
• Autor(a) principal: Szabo, Alexandre
• Orientador(a): Silva, Leandro Nunes de Castro
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Presbiteriana Mackenzie
• Palavras-chave em Português: conjuntos nebulosos do tipo-1; conjuntos nebulosos do tipo-2; agrupamento de dados; combinação de agrupamentos; enxame de partículas
• Palavras-chave em Inglês: type-1 fuzzy sets; type-2 fuzzy sets; data clustering; cluster ensembles; particle swarm
• Área do conhecimento CNPq: CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA
• Link de acesso: http://dspace.mackenzie.br/handle/10899/24263

Clustering usually treats objects as belonging to mutually exclusive clusters, what is usually im-precise, because an object may belong to more than one cluster simultaneously with different membership degrees. The clustering algorithms, both crisp and fuzzy, have a number of parameters to be adjusted so that they present the best performance for a given database. Furthermore, it is known that no single algorithm is better than all the others for all problem classes, and the combi-nation of solutions found by various algorithms (or the same algorithm with different parameters) may lead to a global solution that is better than those found by individual algorithms, including the best one. It is within this context that the present thesis proposes a new fuzzy clustering algo-rithm inspired by the behavior of particle swarms and, then, introduces a new form of combining the clustering algorithms using concepts from Type-2 fuzzy sets.