Skip to main content

Unsupervised Learning-Based Stock Keeping Units Segmentation

  • Conference paper
  • First Online:
Reliability and Statistics in Transportation and Communication (RelStat 2018)

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Ernst, R., Cohen, M.A.: Operations related groups (ORGs): a clustering procedure for production/inventory systems. J. Oper. Manage. 9(4), 574–598 (1990)

    Google Scholar 

  2. Jackson, I., Tolujevs, J., Reggelin, T.: The combination of discrete-event simulation and genetic algorithm for solving the stochastic multi-product inventory optimization problem. Transp. Telecommun. J. 19(3), 233–243 (2018)

    Google Scholar 

  3. Srinivasan, M., Moon, Y.B.: A comprehensive clustering algorithm for strategic analysis of supply chain networks. Comput. Ind. Eng. 36(3), 615–633 (1999)

    Google Scholar 

  4. Egas, C., Masel, D.: Determining warehouse storage location assignments using clustering analysis. In: 11th IMHRC Proceedings on Progress in Material Handling Research, Milwaukee, Wisconsin, USA, pp. 22–33 (2010)

    Google Scholar 

  5. Desai, V.L.: Evaluating Clustering methods for multi-echelon (r, Q) policy setting. In: Proceedings of the IIE Annual Conference, p. 352. Institute of Industrial and Systems Engineers (2007)

    Google Scholar 

  6. Yang, C.L., Nguyen, T.P.Q.: Constrained clustering method for class-based storage location assignment in warehouse. Ind. Manage. Data Syst. 116(4), 667–689 (2016)

    Google Scholar 

  7. GitHub repository “pumpy”. https://github.com/Jackil1993/pumpy. Accessed 10 July 2018

  8. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12(1), 2825–2830 (2011)

    Google Scholar 

  9. Zloba, E., Yatskiv, I.: Statistical methods of reproducing of missing data. Comput. Model. New Technol. 6(1), 51–61 (2002)

    Google Scholar 

  10. Jonsson, P., Wohlin, C.: An evaluation of k-nearest neighbour imputation using likert data. In: 10th International Symposium on Software Metrics, pp. 108–118 (2004)

    Google Scholar 

  11. Chen, J., Shao, J.: Jackknife variance estimation for nearest-neighbor imputation. J. Am. Stat. Assoc. 96(453), 260–269 (2001)

    Google Scholar 

  12. Batista, G.E., Monard, M.C.: A study of K-nearest neighbour as an imputation method. HIS 87, 251–260 (2002)

    Google Scholar 

  13. Mohamad, I.B., Usman, D.: Standardization and its effects on K-means clustering algorithm. Res. J. Appl. Sci. Eng. Technol. 6(17), 3299–3303 (2013)

    Google Scholar 

  14. Campos, G.O., Zimek, A., Sander, J., Campello, R.J., Micenková, B., Schubert, E., Assent, I., Houle, M.E.: On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study. Data Min. Knowl. Disc. 30(4), 891–927 (2016)

    Google Scholar 

  15. Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 603–619 (2002)

    Google Scholar 

  16. Tran, T.N., Drab, K., Daszykowski, M.: Revised DBSCAN algorithm to cluster data with dense adjacent clusters. Chemometr. Intell. Lab. Syst. 120, 92–96 (2013)

    Google Scholar 

  17. Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20, 53–65 (1987)

    Google Scholar 

  18. Caliński, T., Harabasz, J.: A dendrite method for cluster analysis. Commun. Stat. -Theory Methods 3(1), 1–27 (1974)

    Google Scholar 

  19. Yatskiv, I., Gusarova, L.: The methods of cluster analysis results validation. Transp. Telecommun. 6(1), 19–26 (2005)

    Google Scholar 

  20. Cheng, Y.: Mean shift, mode seeking, and clustering. IEEE Trans. Pattern Anal. Mach. Intell. 17(8), 790–799 (1995)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ilya Jackson .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Jackson, I., Avdeikins, A., Tolujevs, J. (2019). Unsupervised Learning-Based Stock Keeping Units Segmentation. In: Kabashkin, I., Yatskiv (Jackiva), I., Prentkovskis, O. (eds) Reliability and Statistics in Transportation and Communication. RelStat 2018. Lecture Notes in Networks and Systems, vol 68. Springer, Cham. https://doi.org/10.1007/978-3-030-12450-2_58

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-12450-2_58

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-12449-6

  • Online ISBN: 978-3-030-12450-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics