Subspace Clustering Based on Self-organizing Map

  • Jin TianEmail author
  • Mengyi Gu
Conference paper


Clustering in high-dimensional data space is a difficult task due to the interference from different dimensions. A dimension may be relevant for some clusters and irrelevant for other data. Subspace clustering aims at finding local cluster structures in certain related subspace. We propose a novel approach to finding subspace clusters based on the trained Self-Organizing Map neural network (SOM). The proposed method takes advantage of nonlinear mapping of SOM and search for subspace clusters on input neurons instead of the whole data space. Experiment results show that the proposed method performs better compared with original SOM and some traditional subspace clustering algorithms.


Self-organizing map Subspace clustering High-dimensional clustering 



The work was supported by the General Program of the National Science Foundation of China (Grant No. 71471127, 71371135).


  1. 1.
    M. Köppen, The curse of dimensionality, in Fifth Online World Conference on Soft Computing in Industrial Applications (2000)CrossRefGoogle Scholar
  2. 2.
    S. Tabakhi, P. Moradi, Relevance–redundancy feature selection based on ant colony optimization. Pattern Recogn. 48(9), 2798–2811 (2015)CrossRefGoogle Scholar
  3. 3.
    R. Agrawal, J.E. Gehrke, D. Gunopulos, P. Raghavan, Automatic subspace clustering of high dimensional data for data mining applications, in Proceedings of the 1998 ACM SIGMOD (Seattle, WA, USA), pp. 94–105Google Scholar
  4. 4.
    H.F. Bassani, A.F.R. Araujo, Dimension selective self-organizing maps with time-varying structure for subspace and projected clustering. IEEE Trans. Neural Netw. Learn. Syst. 26(3), 458–471 (2015)CrossRefGoogle Scholar
  5. 5.
    E. Ller, S. Nnemann, I. Assent, T. Seidl, Evaluating clustering in subspace projections of high dimensional data. Proc. VLDB Endow. 2(1), 1270–1281 (2009)CrossRefGoogle Scholar
  6. 6.
    C.M. Procopiuc, M. Jones, P.K. Agarwal, T.M. Murali, A Monte Carlo algorithm for fast projective clustering, in Proceedings of the 2002 ACM SIGMOD (Madison, WI, USA), pp. 418–427Google Scholar
  7. 7.
    H.P. Kriegel, P. Kröger, M. Renz, S. Wurst, A generic framework for efficient subspace clustering of high-dimensional data, in Fifth IEEE International Conference on Data Mining (Houston, TX, USA, 2005)Google Scholar
  8. 8.
    C.C. Aggarwal, J.L. Wolf, P.S. Yu, C. Procopiuc, J.S. Park, Fast algorithms for projected clustering, in Proceedings of the 1999 ACM SIGMOD (Philadelphia, PA, USA), pp. 61–72Google Scholar
  9. 9.
    A.Y. Yang, J. Wright, Y. Ma, S.S. Sastry, Unsupervised segmentation of natural images via lossy data compression. Comput. Vis. Image Underst. 110(2), 212–225 (2008)CrossRefGoogle Scholar
  10. 10.
    D. Jiang, C. Tang, A. Zhang, Cluster analysis for gene expression data: a survey. IEEE Trans. Knowl. Data Eng. 16(11), 1370–1386 (2004)CrossRefGoogle Scholar
  11. 11.
    P.B. Chou, E. Grossman, D. Gunopulos, P. Kamesam, Identifying prospective customers, in Proceedings of the 2000 ACM SIGKDD (Boston, MA, USA), pp. 447–456Google Scholar
  12. 12.
    T. Kohonen, Essentials of the self-organizing map. Neural Netw. 37(1), 52–65 (2013)CrossRefGoogle Scholar
  13. 13.
    T. Kohonen, Self-organized formation of topologically correct feature maps. Biol. Cybern. 43(1), 59–69 (1982)CrossRefGoogle Scholar
  14. 14.
    E. Müller, I. Assent, S. Günnemann, T. Seidl, OpenSubspace: an open source framework for evaluation and exploration of subspace clustering algorithms in WEKA, in Proceedings of 1st Open Source in Data Mining Workshop, OSDM’09 (Bangkok, Thailand), pp. 2–13Google Scholar
  15. 15.
    M. Lichman, UCI Machine Learning Repository, (University of California, School of Information and Computer Science, Irvine, CA, 2013)

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.College of Management and EconomicsTianjin UniversityTianjinChina

Personalised recommendations