Unsupervised Learning-Based Stock Keeping Units Segmentation

Ilya Jackson, Aleksandrs Avdeikins, Jurijs Tolujevs 

In: Kabashkin I., Yatskiv I., Prentkovskis O. (eds) Reliability and Statistics in Transportation and Communication. RelStat 2018. Lecture Notes in Networks and Systems, vol 68. Springer, Cham
DOI: 10.1007/978-3-030-12450-2_58
: Clustering, Inventory segmentation, Inventory clustering, Data mining, Unsupervised machine learning, Principal component analysis

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This paper reports on the unsupervised learning approach for solving stock keeping units segmentation problem. The dataset under consideration contains 2279 observations with 9 features. Since the “ground truth” is not known, the research aims to compare such clustering algorithms as K-means, mean-shift and DBSCAN based only on the internal evaluation, thus, this research may be considered as descriptive cluster analysis. Besides that, several preprocessing techniques are utilized in order to improve the result.