A Deep Clustering Algorithm Based on Self-organizing Map Neural Network

  • Yanling Tao
  • Ying Li
  • Xianghong LinEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10956)


Clustering is one of the most basic unsupervised learning problems in the field of machine learning and its main goal is to separate data into clusters with similar data points. Because of various redundant and complex structures for the raw data, the general algorithm usually is difficult to separate different clusters from the data and the effect is not obvious. Deep learning is a technology that automatically learns nonlinear and more conducive clustering features from complex data structures. This paper presents a deep clustering algorithm based on self-organizing map neural network. This method combines the feature learning ability of stacked auto-encoder from the raw data and feature clustering with unsupervised learning of self-organizing map neural network. It is aim to achieve the greatest separability for the data space. Through the experimental analysis and comparison, the proposed algorithm has better recognition rate, and improves the clustering performance on low and high dimension data.


Clustering algorithm Deep neural networks Stacked auto-encoders Self-organizing map neural network 



The work is supported by the National Natural Science Foundation of China under Grant No. 61762080, and the Medium and Small Scale Enterprises Technology Innovation Foundation of Gansu Province under Grant No. 17CX2JA038.


  1. 1.
    Lin, Y., Hang, L., Li, X., et al.: Deep learning in NLP: methods and applications. J. Univ. Electron. Sci. Technol. China 46(6), 913–919 (2017)Google Scholar
  2. 2.
    Gheisari, M., Wang, G., Bhuiyan, M.Z.A.: A survey on deep learning in big data. In: IEEE International Conference on Computational Science and Engineering, pp. 173–180. IEEE, Guangzhou, China (2017)Google Scholar
  3. 3.
    Shen, D., Wu, G., Suk, H.I.: Deep learning in medical image analysis. Ann. Rev. Biomed. Eng. 19(1), 221–248 (2017)CrossRefGoogle Scholar
  4. 4.
    Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2014)CrossRefGoogle Scholar
  5. 5.
    LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)CrossRefGoogle Scholar
  6. 6.
    Jain, A.K.: Data clustering: a review. ACM Comput. Surv. 31(3), 264–323 (2000)CrossRefGoogle Scholar
  7. 7.
    Xu II, R.: D.W.: Survey of clustering algorithms. IEEE Trans. Neural Netw. 16(3), 645–678 (2005)CrossRefGoogle Scholar
  8. 8.
    Torre, F.D.L., Kanade, T.: Discriminative cluster analysis. In: Caruana, R., Niculescu-Mizil, A. (eds.) Proceedings of the 23rd International Conference on Machine Learning, pp. 241–248. ACM (2006)Google Scholar
  9. 9.
    Dilokthanakul, N., Mediano, P.A.M., Garnelo, M., et al.: Deep unsupervised clustering with gaussian mixture variational autoencoders. arXiv preprint arXiv:1611.02648 (2016)
  10. 10.
    Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representations by error propagation. Nature 323(6088), 533–536 (1986)CrossRefGoogle Scholar
  11. 11.
    Badino, L., Canevari, C., Fadiga, L., et al.: An auto-encoder based approach to unsupervised learning of subword units. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 7634–7638. IEEE, Florence, Italy (2014)Google Scholar
  12. 12.
    Bengio, Y.: Learning deep architectures for AI. Found. Trends Mach. Learn. 2, 1–127 (2009)CrossRefGoogle Scholar
  13. 13.
    Kohonen, T.: Automatic formation of topological maps of patterns in a self-organizing system. In: Oja, E., Simula, O. (eds.) Proceedings of 2SCIA, Scandinavian Conference on Image Analysis, pp. 214–220. Helsinki, Finland (1981)Google Scholar
  14. 14.
    Kohonen, T.: Self-organized formation of topologically correct feature maps. Biol. Cybern. 43(1), 59–69 (1982)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Yang, Y., Xu, D., Nie, F., et al.: Image clustering using local discriminant models and global integration. IEEE Tran. Image Process. 19(10), 2761–2773 (2010)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Kuhn, H.W.: The Hungarian method for the assignment problem. Nav. Res. Logistics 2(1–2), 83–97 (1955)MathSciNetCrossRefGoogle Scholar

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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Social Development and Public AdministrationNorthwest Normal UniversityLanzhouChina
  2. 2.College of Computer Science and EngineeringNorthwest Normal UniversityLanzhouChina

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