[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content

Advertisement

Springer Nature Link
Log in

A deep learning object detection method to improve cluster analysis of two-dimensional data

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Clustering is an unsupervised machine learning method grouping data samples into clusters of similar objects, used as a system support tool in numerous applications such as banking customers profiling, document retrieval, image segmentation, and e-commerce recommendation engines. The effectiveness of several clustering techniques is sensible to the initialization parameters, and different solutions have been proposed in the literature to overcome this limitation. They require high computational memory consumption when dealing with big data. In this paper, we propose the application of a recent object detection Deep Learning model (YOLO-v5) for assisting the initialization of classical techniques and improving their effectiveness on two-variate datasets, leveraging the accuracy and reducing dramatically the memory and time consumption of classical clustering methods.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Data availability

The code to generate data is available here: https://github.com/rcouturier/data4clustering

References

  1. Xu D, Yingjie T (2015) A comprehensive survey of clustering algorithms. Annals of Data Science 2(2):165–193

    Article  Google Scholar 

  2. Guyeux C, Chrétien S, Bou Tayeh G, Demerjian J, Bahi J (2019) Introducing and comparing recent clustering methods for massive data management in the internet of things. J Sensor Actuator Netw, 8(4):56 (25)

  3. Yang M-S, Lai C-Y, Lin C-Y (2012) A robust em clustering algorithm for gaussian mixture models. Pattern Recogn 45(11):3950–3961

    Article  Google Scholar 

  4. Sinaga KP, Yang M-S (2020) Unsupervised k-means clustering algorithm. IEEE Access, 8:80716–80727

  5. Nyo MT, Mebarek-Oudina F, Hlaing SS, Khan NA (2022) Otsu’s thresholding technique for mri image brain tumor segmentation. Multimed Tools Appl 81(30):43837–43849

  6. Fahad A, Alshatri N, Tari Z, Alamri A, Khalil I, Zomaya AY, Foufou S, Bouras A (2014) A survey of clustering algorithms for big data: Taxonomy and empirical analysis. IEEE Trans Emerg Top Comput 2(3):267–279

    Article  Google Scholar 

  7. Barioni MCN, Razente H, Marcelino AMR, Traina AJM, Traina CJ (2014) Open issues for partitioning clustering methods: an overview. Wiley Interdisc Rev Data Mining Know Discov 4(3):161–177

    Article  Google Scholar 

  8. Alhawarat M, Hegazi M (2018) Revisiting k-means and topic modeling, a comparison study to cluster arabic documents. IEEE Access 6:42740–42749

    Article  Google Scholar 

  9. Yinfeng M, Jiye L, Fuyuan C, Yijun H (2018) A new distance with derivative information for functional k-means clustering algorithm. Inf Sci 463:166–185

    Google Scholar 

  10. Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the em algorithm. J Roy Stat Soc B 39(1):1–38

    Article  MathSciNet  Google Scholar 

  11. MacQueen JB (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of 5-th berkeley symposium on mathematical statistics and probability, Berkeley, University of California Press, pp 281–297

  12. Stuart L (1982) Least squares quantization in pcm. IEEE Trans Inf Theory 28(2):129–137

    Article  MathSciNet  Google Scholar 

  13. Arthur D, Vassilvitskii S (2006) k-means++: the advantages of careful seeding. Techn Rep 2006–13 Stanford InfoLab

  14. Pelleg D, Moore A (2000) X-means: Extending k-means with efficient estimation of the number of clusters. In: Proceedings of the 17th international conference on machine learning, Citeseer, pp 727

  15. Dunn JC (1973) A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters

  16. James C (1981) Bezdek. Pattern Recognition with Fuzzy Objective Function Algorithms. Springer, US

    Google Scholar 

  17. Yeung KY, Ruzzo WL (2001) Details of the adjusted rand index and clustering algorithms, supplement to the paper an empirical study on principal component analysis for clustering gene expression data. Bioinformatics, 17(9):763–774

  18. Kass RE Raftery AE (1995) Bayes factors. J Am Stati Assoc 90(430):773–795

  19. Dziopa T (2016) Clustering validity indices evaluation with regard to semantic homogeneity. In: FedCSIS (Position Papers), pp 3–9

  20. Hamparsum B (1987) Model selection and akaike’s information criterion (aic): the general theory and its analytical extensions. Psychometrika 52(3):345–370

  21. Davies DL, Bouldin DW (1979) A cluster separation measure. IEEE Trans Pattern Anal Mach Intell (2):224–227

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

  23. Caliński T, Harabasz J (1974) A dendrite method for cluster analysis. Communications in Statistics-theory and Methods 3(1):1–27

  24. Tibshirani R, Walther G, Hastie T (2001) Estimating the number of clusters in a data set via the gap statistic. J Royal Stat Soc Ser B (Statistical Methodology) 63(2):411–423

  25. Rendón E, Abundez I, Arizmendi A, Quiroz EM (2011) Internal versus external cluster validation indexes. Int J Comput Commun 5(1):27–34

  26. Yolov5 in pytorch, 06:2020. https://github.com/ultralytics/yolov5

  27. Imambi S, Prakash KB, Kanagachidambaresan GR (2021) Pytorch. In: Programming with TensorFlow, Springer, pp 87–104

  28. Ketkar N, Moolayil J (2021) Introduction to pytorch. In: Deep learning with python, Springer, pp 27–91

  29. Likas A, Vlassis N, Verbeek JJ (2003) The global k-means clustering algorithm. Pattern Recogn 36(2):451–461

  30. Pelleg D, Moore AW et al (2000) X-means: extending k-means with efficient estimation of the number of clusters. In: Icml, vol 1, pp 727–734

  31. Maji P, Pal SK (2007) Rfcm: a hybrid clustering algorithm using rough and fuzzy sets. Fundamenta Informaticae 80(4):475–496

Download references

Acknowledgements

This work was partially funded by project ANER 2022 AGRO-IA-LIMENTAIRE and the EIPHI Graduate School (contract ANR-17-EURE-0002). The Mesocentre of Franche-Comté provided the computing facilities. This work was also partially sponsored by the General Directorate for Scientific Research and Technological Development, Ministry of Higher Education and Scientific Research (DGRSDT), Algeria.

Author information

Authors and Affiliations

  1. FEMTO-ST Institute, CNRS, University of Franche-Comte (UFC), Besançon, France

    Raphaël Couturier & Hassan Noura

  2. Instituto de Matemáticas y Aplicaciones de Castellón, Departamento de Matemáticas, Universitat Jaume I de Castellón, E-12071, Castellón, Spain

    Pablo Gregori

  3. Electrical and Computer Engineering Department, American University of Beirut, Beirut, Lebanon

    Ola Salman

  4. LIMED Laboratory, Faculty of Exact Sciences, University of Bejaia, 06000, Bejaia, Algeria

    Abderrahmane Sider

Authors
  1. Raphaël Couturier
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pablo Gregori
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hassan Noura
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ola Salman
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Abderrahmane Sider
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Raphaël Couturier.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Couturier, R., Gregori, P., Noura, H. et al. A deep learning object detection method to improve cluster analysis of two-dimensional data. Multimed Tools Appl 83, 71171–71187 (2024). https://doi.org/10.1007/s11042-024-18148-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-024-18148-5

Keywords

Search

Navigation