Data mining classification models for industrial planning

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. Severa...

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Bibliographic Details
Main Author: Braganca, Ricardo (author)
Other Authors: Portela, Filipe (author), Vale, A. (author), Guimarães, Tiago André Saraiva (author), Santos, Manuel (author)
Format: conferencePaper
Language:eng
Published: 2017
Subjects:
Data mining
Classification
CRISP-DM
DSR
Lean
WEKA
Ciências Naturais::Ciências da Computação e da Informação
Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
Science & Technology
Online Access:http://hdl.handle.net/1822/67826
Country:Portugal
Oai:oai:repositorium.sdum.uminho.pt:1822/67826
Description
Summary: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.

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