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Wojtkowiak Krzysztof (Nicolaus Copernicus University in Toruń)
Data Mining Analytics Fundamentals and Their Application in Logistics
Acta Universitatis Nicolai Copernici. Zarządzanie, 2020, t. 47, nr 1, s. 47-63, rys., tab., bibliogr. 18 poz.
Słowa kluczowe
Data Mining, Logistyka
Data Mining, Logistics
Klasyfikacja JEL: L91
The article describes several basic data mining fundamentals and their application in logistics and it consists of two sections. The first one is a description of different parts of data mining process: preparing the input data, completing the missing data, classification method using k-nearest neighbours algorithm with theoretical examples of usage conducted in opensource software called R and Weka. The second section of the article focuses on theoretical application of data mining methods in logistics, mainly in solving transportation problems and enhancing customer's satisfaction. This section was strongly influenced by data provided by DHL enterprise report on Big Data. The data used in theoretical examples is of own elaboration. (original abstract)
Pełny tekst
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  12. Paul A., Saravanan V., Ranjit Jeba Thangaiah P., (2011), Data Mining Analytics to Minimize Logistics Cost, International Journal of Advances in Science and Technology 2/3.
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