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Data Mining Classification Models for Industrial Planning

  • Ricardo Bragança
  • Filipe PortelaEmail author
  • A. Vale
  • Tiago Guimarães
  • Manuel Santos
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 721)

Abstract

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.

Keywords

Data mining Classification CRISP-DM DSR Lean WEKA 

Notes

Acknowledgements

This work has been supported by Compete: POCI-01-0145-FEDER-007043 and FCT - Fundação para a Ciência e Tecnologia within the Project Scope UID/CEC/00319/2013.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  • Ricardo Bragança
    • 1
  • Filipe Portela
    • 1
    Email author
  • A. Vale
    • 2
  • Tiago Guimarães
    • 1
  • Manuel Santos
    • 1
  1. 1.Algoritmi Research CentreUniversity of MinhoGuimarãesPortugal
  2. 2.Value Added PartnersPortoPortugal

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