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Unsupervised Learning-Based Stock Keeping Units Segmentation

  • Ilya JacksonEmail author
  • Aleksandrs Avdeikins
  • Jurijs Tolujevs
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 68)

Abstract

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.

Keywords

Clustering Inventory segmentation Inventory clustering Data mining Unsupervised machine learning Principal component analysis 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ilya Jackson
    • 1
    Email author
  • Aleksandrs Avdeikins
    • 1
    • 2
  • Jurijs Tolujevs
    • 1
  1. 1.Transport and Telecommunication InstituteRigaLatvia
  2. 2.Trialto Latvia LTD, “Dominante”Kekavas n.Latvia

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