Skip to main content
SpringerLink
Log in

Fuzzy Clustering with Self-growing Net

  • Published:
International Journal of Fuzzy Systems Aims and scope Submit manuscript

Abstract

A novel deep feature mapping method self-growing net (SG-Net) is proposed, and its combination with classical fuzzy c-means (FCM) called SG-Net-FCM is further developed. SG-Net is a feedforward learning structure for nonlinear explicit feature mapping and includes four types of layers, i.e., input, fuzzy mapping, hybrid, and output layers. The fuzzy mapping layer maps the data from input layer to a high-dimensional feature space using TSK fuzzy mapping, i.e. the fuzzy mapping of Takagi–Sugeno–Kang fuzzy system (TSK-FS). Afterward, each layer in SG-Net accepts additional inputs from all preceding layers and provides its own distinguished features by using principal component analysis to all subsequent layers. The final output of SG-Net is fed to FCM. Since SG-Net-FCM is developed based on the TSK fuzzy mapping, it is more interpretable than classical kernelized fuzzy clustering methods. The effectiveness of the proposed clustering algorithm is experimentally verified on UCI datasets.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Xu, R.: Survey of clustering algorithms. IEEE Press, New Jersey (2005)

    Book  Google Scholar 

  2. Ying, W., Chung, F.L., Wang, S.: Scaling up synchronization-inspired partitioning clustering. IEEE Trans. Knowl. Data Eng. 26(8), 2045–2057 (2014)

    Article  Google Scholar 

  3. Fazendeiro, P., Oliveira, J.V.D.: Observer-biased fuzzy clustering. IEEE Trans. Fuzzy Syst. 23(1), 85–97 (2015)

    Article  Google Scholar 

  4. Chen, S., Wang, J., Zhang, H.: A hybrid PSO-SVM model based on clustering algorithm for short-term atmospheric pollutant concentration forecasting. Technol. Forecast. Soc. Change 146, 41–54 (2019)

    Article  Google Scholar 

  5. Zhang, X., Liu, H., Liu, X.: Multi-task multi-view clustering. IEEE Trans. Knowl. Data Eng. 28(12), 3324–3338 (2016)

    Article  Google Scholar 

  6. Zhou, S., Xu, Z., Liu, F.: Method for determining the optimal number of clusters based on agglomerative hierarchical clustering. IEEE Trans. Neural Netw. Learn. Syst. 28(12), 3007–3017 (2017)

    Article  MathSciNet  Google Scholar 

  7. Ayed AB, Halima MB, Alimi AM. Adaptive fuzzy exponent cluster ensemble system based feature selection and spectral clustering. In: IEEE International Conference on Fuzzy Systems, IEEE, pp. 1–6 (2017)

  8. Jia, H., Cheung, Y.M.: Subspace clustering of categorical and numerical data with an unknown number of clusters. IEEE Trans. Neural Netw. Learn. Syst. 29(8), 3308–3325 (2018)

    Article  MathSciNet  Google Scholar 

  9. Girolami, M.: Mercer kernel-based clustering in feature space. IEEE Trans. Neural Netw. 13(3), 780–784 (2002)

    Article  Google Scholar 

  10. Wu, H., Kato, T., Yamada, T., et al.: Personal sleep pattern visualization using sequence-based kernel self-organizing map on sound data. Artif. Intell. Med. 80, 1 (2017)

    Article  Google Scholar 

  11. Qinand AK, Suganthan PN. Kernel neural gas algorithms with application to cluster analysis. In: International Conference on Pattern Recognition, IEEE Computer Society, pp. 617–620 (2004)

  12. Ding, Y., Fu, X.: Kernel-based fuzzy c-means clustering algorithm based on genetic algorithm. Neurocomputing 188, 233–238 (2016)

    Article  Google Scholar 

  13. Wang, J., Deng, Z., Choi, K.S., et al.: Distance metric learning for soft subspace clustering in composite kernel space. Pattern Recognit. 52(C), 113–134 (2016)

    Article  Google Scholar 

  14. Huang, H.C., Chuang, Y.Y., Chen, C.S.: Multiple kernel fuzzy clustering. IEEE Trans. Fuzzy Syst. 20(1), 120–134 (2012)

    Article  Google Scholar 

  15. Deng, Z., Choi, K.S., Chung, F.L., et al.: Scalable TSK fuzzy modeling for very large datasets using minimal-enclosing-ball approximation. IEEE Trans. Fuzzy Syst. 19(2), 210–226 (2011)

    Article  Google Scholar 

  16. Luo, M.: Hierarchical structured sparse representation for T-S fuzzy systems identification. IEEE Trans. Fuzzy Syst. 21(6), 1032–1043 (2013)

    Article  Google Scholar 

  17. Luo, M., Sun, F., Liu, H.: Joint block structure sparse representation for multi-input–multi-output (MIMO) T–S fuzzy system identification. IEEE Trans. Fuzzy Syst. 22(6), 1387–1400 (2014)

    Article  Google Scholar 

  18. Lin D, Wang J, Zhu J, et al. A novel Takagi–Sugeno fuzzy system modeling method with joint feature selection and rule reduction. In: IEEE International Conference on Fuzzy Systems, IEEE, pp. 1–7 (2018)

  19. Deng, Z., Cao, L., Jiang, Y., et al.: Minimax probability TSK fuzzy system classifier: a more transparent and highly interpretable classification model. IEEE Trans. Fuzzy Syst. 23(4), 813–826 (2015)

    Article  Google Scholar 

  20. Wang, S., Chung, F.L., Wang, J., et al.: A fast learning method for feedforward neural networks. Neurocomputing 149, 295–307 (2015)

    Article  Google Scholar 

  21. Huang G, Liu Z, Laurens VDM, et al. Densely connected convolutional networks. In: IEEE Conference on Computer Vision and Pattern Recognition, IEEE, No. 1, pp. 2261–2269 (2017)

  22. Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)

  23. Zeiler MD, Fergus R. Visualizing and understanding convolutional networks. In: European Conference on Computer Vision, Springer, Cham, pp. 818–833 (2014)

  24. Hinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)

    Article  MathSciNet  Google Scholar 

  25. Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions. In: IEEE Conference on Computer Vision and Pattern Recognition, IEEE, pp. 1–9 (2015)

  26. He K, Zhang X, Ren S, et al. Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, IEEE, pp. 770–778 (2016)

  27. Le QV. Building high-level features using large scale unsupervised learning. In: International Conference on Acoustics, Speech and Signal Processing, IEEE, pp. 8595–8598 (2013)

  28. Wang, J., Liu, H., Qian, X., et al.: Cascaded hidden space feature mapping, fuzzy clustering, and nonlinear switching regression on large datasets. IEEE Trans. Fuzzy Syst. 26(2), 640–655 (2018)

    Article  Google Scholar 

  29. Wold, S., Esbensen, K., Geladi, P.: Principal component analysis. Chemom. Intell. Lab. Syst. 2(1), 37–52 (1987)

    Article  Google Scholar 

  30. Hwang, C.L., Jan, C.: Recurrent-neural-network-based multivariable adaptive control for a class of nonlinear dynamic systems with time-varying delay. IEEE Trans. Neural Netw. Learn. Syst. 27(2), 388–401 (2016)

    Article  MathSciNet  Google Scholar 

  31. Estévez, P.A., Tesmer, M., Perez, C.A., et al.: Normalized mutual information feature selection. IEEE Trans. Neural Netw. 20(2), 189–201 (2009)

    Article  Google Scholar 

  32. Shao, J., He, X., Böhm, C., et al.: Synchronization-inspired partitioning and hierarchical clustering. IEEE Trans. Knowl. Data Eng. 25(4), 893–905 (2013)

    Article  Google Scholar 

  33. Bezdek, J.C., Ehrlich, R., Full, W.: FCM: the fuzzy c-means clustering algorithm. Comput. Geosci. 10(2), 191–203 (1984)

    Article  Google Scholar 

  34. He, Q., Xin, J., Du, C., et al.: Clustering in extreme learning machine feature space. Neurocomputing 128(5), 88–95 (2014)

    Article  Google Scholar 

  35. Graves, D., Pedrycz, W.: Kernel-based fuzzy clustering and fuzzy clustering: a comparative experimental study. Fuzzy Sets Syst. 161(4), 522–543 (2010)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

This work was supported by the Natural Science Foundation of Jiangsu Province under Grant BK20161268 and BK20181339, and by the Humanities and Social Sciences Foundation of the Ministry of Education under Grant 18YJCZH229, and in part by the 13th Five-Year Plan Project of Educational Science in Jiangsu Province under Grant X-a/2018/10, and in part by the Research Projects of Philosophy and Social Sciences in Colleges and Universities of Jiangsu Province under Grant 2018SJSZ439, and in part by the Fujian Provincial Leading Project under Grant 2017H0030, and in part by the Key Project of College Youth Natural Science Foundation of Fujian Province under Grant JZ160467.

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Changshu Institute of Technology, Changshu, 215500, China

    Wenhao Ying

  2. Shanghai Institute for Advanced Communication and Data Science, School of Communication and Information Engineering, Shanghai University, Shanghai, China

    Jun Wang

  3. School of Digital Media, Jiangnan University, Wuxi, 214122, China

    Zhaohong Deng

  4. Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Minjiang University, Fuzhou, 350121, China

    Jun Wang, Fuquan Zhang & Zuoyong Li

  5. School of Computer Science & Technology, Beijing Institute of Technology, Beijing, 100081, China

    Fuquan Zhang

  6. Fujian Province Collaborative Innovation Center of Traditional Chinese Medicine Health Management 2011, Fujian University of Traditional Chinese Medicine, Fuzhou, 350122, China

    Zuoyong Li

Authors
  1. Wenhao Ying
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jun Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhaohong Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Fuquan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zuoyong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jun Wang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ying, W., Wang, J., Deng, Z. et al. Fuzzy Clustering with Self-growing Net. Int. J. Fuzzy Syst. 22, 450–460 (2020). https://doi.org/10.1007/s40815-019-00782-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40815-019-00782-z

Keywords

Search

Navigation