DATA MINING ANALYTICS FUNDAMENTALS AND THEIR APPLICATION IN LOGISTICS

Abstract

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.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