Mineração de preferências contextuais fuzzy

• Ano de defesa: 2014
• Autor(a) principal: Costa, Juliete Aparecida Ramos
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Uberlândia; BR; Programa de Pós-graduação em Ciência da Computação; Ciências Exatas e da Terra; UFU
• Palavras-chave em Português: Preferências contextuais; Relações de preferências fuzzy; Mineração de preferências; Métodos de reparação de inconsistência; Mineração de dados (Computação); Algoritmos de computador; Contextual preferences; Fuzzy preference relations; Preference mining; Methods for repair inconsistency; CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: https://repositorio.ufu.br/handle/123456789/12563; https://doi.org/10.14393/ufu.di.2014.310

In recent years, much researches in Preference Mining has focused on development of methods for mining a preference model from crisp pairwise representation. In this research, is presented the FuzzyPrefMiner algorithm, developed for mining fuzzy contextual preference model from fuzzy preference relation. This type of preference representation is composed by a set of triples (u; v; n), where, u and v are tuples evaluated by user and n is the degree of preference of tuple u with respect to v. The fuzzy preference relations enable the study of the consistency of users and in this context, are presented two methods for repair inconsistency of these relations: No Incremental Range Voting and Incremental Range Voting. Both methods are based on technical Voting System and are used for repair the inconsistency of fuzzy preference relations inferred by FuzzyPrefMiner. A set of experiments in real data were performed to validate the FuzzyPrefMiner algorithm and the methods for repair inconsistency. The results quite satisfactory when compared with the state of the art regarding Preference Mining Algorithms and Methods for Repair Inconsistency.