Krzysztof Juliusz Wojtkowiak



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 open-source 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.

Słowa kluczowe

logistyka; data mining

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Beier F.I., Rutkowski K., (2003), Logistyka, Szkoła Główna Handlowa w Warszawie, Warszawa.

Berry M., Linoff G., (2004), Data Mining Techniques For Marketing, Sales and Customer Relationship Management, Wiley Publishing, Indianapolis, Indiana.

Everett J. E., (2001), Iron ore production scheduling to improve product quality, “European Journal of Operational Research”, 129/2.

Fayyad U.M., Piatetsky-Shapiro G., Smyth P., Uthurusamy R., (1996), Advances in Knowledge Discovery and Data Mining, MIT Press, Cambridge, Massachusetts.

Han J., Fu Y., Wang W., Chiang J., Gong W., Koperski K., Li D., Lu Y., Rajan A., Stefanovic N., Xia B., Zaiane O.R., (1996), DBMiner: A System for Mining Knowledge in Large Relational Databases, Portland, Oregon.

Jain A.K., Murty M.N., Flynn P.J., (1999), Data clustering: a review, “ACM Computing Surveys” 31/3.

Jeske M., Gruner M, Weiß F., (2013), Big data in Logistics, a DHL perspective on how to move beyond the hype, DHL, Troisdorf.

Klepac G., (2014), Data mining models as a tool for churn reduction and custom product development in telecommunication industries in: Vasant P., Handbook of research on novel soft computing intelligent algorithms: theory and practical application, IGI Global, Hershey.

Langer L., Van der Kwast T., Evans A., Trachtenberg J., Wilson B., Haider M., (2009), Prostate Cancer Detection With Multi-parametric MRI: Logistic Regression Analysis of Quantitative T2, Diffusion-Weighted Imaging, and Dynamic Contrast-Enhanced MRI, “Journal of Magnetic Resonance Imaging” 30.

Larose, D.T, (2006), Odkrywanie wiedzy z danych. Wprowadzenie do eksploracji danych, PWN, Warszawa.

Mitchell T. M., (1997), Machine learning, McGraw-Hill Science/Engineering/Math.

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.

Silva R.F., Cugnasca C.E, (2015), What is the importance of data mining for logistics and supply chain management ? A bibliometric review from 2000 to 2014.



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ISSN (print) 1689-8966
ISSN (online) 2450-7040

Action funded by the Ministry of Science and Higher Education under the contract number 916/P-DUN/2019 by funds dedicated to dissemination of research findings. Preparing for publication papers in English and employing reviewers affiliated in research institutions abroad in 8 issues of the journal Acta Universitatis Nicolai Copernici Zarządzanie in 2019-2020: Vol. 46, No. 1-4 (2019), Vol. 47, No. 1-4 (2020)

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