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Data mining classification models for industrial planning

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Resumo:The data mining models are an excellent tool to help companies that live from the sale of items they produce. With these models combined with Lean Production, it becomes easier to remove waste and optimize industrial production. This project is based on the phases of the methodology CRISP-DM. Several methods were applied to this data namely, average, mean and standard deviation, quartiles and Sturges rule. Classification Techniques were used in order to understand which model has the best probability of hitting the correct result. After performing the tests, model M1 was the one with the best chance to accomplish a great level of classification having 99.52% of accuracy.
Autores principais:Braganca, Ricardo
Outros Autores:Portela, Filipe; Vale, A.; Guimarães, Tiago André Saraiva; Santos, Manuel
Assunto:Data mining Classification CRISP-DM DSR Lean WEKA
Ano:2017
País:Portugal
Tipo de documento:comunicação em conferência
Tipo de acesso:acesso restrito
Instituição associada:Universidade do Minho
Idioma:inglês
Origem:RepositóriUM - Universidade do Minho

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