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https://hdl.handle.net/1822/67826
Título: | Data mining classification models for industrial planning |
Autor(es): | Braganca, Ricardo Portela, Filipe Vale, A. Guimarães, Tiago André Saraiva Santos, Manuel |
Palavras-chave: | Data mining Classification CRISP-DM DSR Lean WEKA |
Data: | 2017 |
Editora: | Springer |
Revista: | Communications in Computer and Information Science |
Resumo(s): | 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. |
Tipo: | Artigo em ata de conferência |
URI: | https://hdl.handle.net/1822/67826 |
ISBN: | 9789811054266 |
DOI: | 10.1007/978-981-10-5427-3_60 |
ISSN: | 1865-0929 |
Versão da editora: | https://link.springer.com/chapter/10.1007%2F978-981-10-5427-3_60 |
Arbitragem científica: | yes |
Acesso: | Acesso restrito UMinho |
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Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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2016 - ICACDS - Data Mining Classification Models for Industrial Planning.pdf Acesso restrito! | 457 kB | Adobe PDF | Ver/Abrir |