Aplicação da mineração de dados na descoberta de padrões do perfil de alunos do curso de SI-UnUCET-UEG

• Ano de defesa: 2012
• Autor(a) principal: Del-fiaco, Ronaldo de Castro
• Orientador(a): Vieira, Sibelius Lellis
• Banca de defesa: Silva, Solange da , Martins, Wellington Santos , Dantas, Maria José Pereira
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Pontifícia Universidade Católica de Goiás
• Programa de Pós-Graduação: Programa de Pós-Graduação STRICTO SENSU em Engenharia de Produção e Sistemas
• Departamento: Engenharia
• País: BR
• Palavras-chave em Português: Mineração de dados; Descoberta de conhecimento; WEKA; Classificação
• Palavras-chave em Inglês: Data Mining; Knowledge Discovery; WEKA; Classification
• Área do conhecimento CNPq: CNPQ::ENGENHARIAS::ENGENHARIA DE PRODUCAO
• Link de acesso: https://tede2.pucgoias.edu.br/handle/tede/2434

The Data Mining (DM) is a part of the process of Knowledge Discovery in Databases. Its implementation requires knowledge of various areas such as computer sciences, statistics, management sciences and the business itself. In particular, it can be applied to discover knowledge that allows the manager to improve the quality of the learning process in which he/she is involved. This work presents the theoretical background of data mining, describes and analyzes a case study where the main objective is to apply the Cross-Industry Standard Process for Data Mining - CRISP-DM methodology to identify the profile of the student that graduates in due time from the course of Bachelor of Information Systems of the State University of Goiás at Anápolis. It describes the data preparation that is used in the process and identify the best proposals for analysis of the case study. As input data, both the transcripts and the answers of the socioeconomic and cultural questionnaire applied to the students are used as attributes for the evaluation of decision tree algorithms implemented in the data mining tool known as WEKA. It was possible to realize that data mining requires a professional who mastered the theory of DM to correctly calibrate the tools and extensive knowledge of the business in order to determine the data mining goals and interpret the results.