Uso de medidas de desempenho e de grau de interesse para análise de regras descobertas nos classificadores

• Ano de defesa: 2008
• Autor(a) principal: Rocha, Mauricio Rêgo Mota da
• Orientador(a): Omar, Nizam
• Banca de defesa: Lima, Clodoaldo Aparecido de Moraes , Pimentel, Edson Pinheiro
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Presbiteriana Mackenzie
• Programa de Pós-Graduação: Engenharia Elétrica
• Departamento: Engenharia Elétrica
• País: BR
• Palavras-chave em Português: descoberta de conhecimento em banco de dados; mineração de dados; pós-processamento; classificação; medidas de avaliação de regras
• Palavras-chave em Inglês: knowledge discovery in databases; data mining; post-processing; classification; rule evaluation measures
• Área do conhecimento CNPq: CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA
• Link de acesso: http://dspace.mackenzie.br/handle/10899/24405

The process of knowledge discovery in databases has become necessary because of the large amount of data currently stored in databases of companies. They operated properly can help the managers in decision-making in organizations. This process is composed of several steps, among them there is a data mining, stage where they are applied techniques for obtaining knowledge that can not be obtained through traditional methods of analysis. In addition to the technical, in step of data mining is also chosen the task of data mining that will be used. The data mining usually produces large amount of rules that often are not important, relevant or interesting to the end user. This makes it necessary to review the knowledge discovered in post-processing of data. In the stage of post-processing is used both measures of performance but also of degree of interest in order to sharpen the rules more interesting, useful and relevant. In this work, using a tool called WEKA (Waikato Environment for Knowledge Analysis), were applied techniques of mining, decision trees and rules of classification by the classification algorithms J48.J48 and J48.PART respectively. In the post-processing data was implemented a package with functions and procedures for calculation of both measures of performance but also of the degree of interest rules. At this stage consultations have also been developed (querys) to select the most important rules in accordance with measures of performance and degree of interest.