Skip to main content
SpringerLink
Log in
SpringerLink
Log in

Balanced Spectral Clustering Algorithm Based on Feature Selection

  • Conference paper
  • First Online:
Advanced Data Mining and Applications (ADMA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13088))

Included in the following conference series:

  • 968 Accesses

Abstract

High dimensional data clustering faced some problems such as sparse samples, difficulty in calculating similarity and so on. In addition, the clustering results sometimes be extremely unbalanced with too many or too few category samples. Therefore, we propose a novel algorithm, that is a balanced spectral clustering algorithm based on feature selection. Firstly, the least square method is used to calculate the target loss error. Secondly, the method of feature selection is used to reduce the influence of noise and redundant features. Thirdly, a balanced regularization term exclusive lasso is introduced to balance the clustering results. Finally, the locality preserving projection is used to maintain the feature structure of the samples. A large number of experimental results show that the proposed algorithm outperformed the comparison algorithms on the two indicators (accuracy and normal mutual information) in most cases, which proves the effectiveness of the proposed spectral clustering algorithm.

This work is partially supported by the Project of Guangxi Science and Technology (GuiKeAD20159041); the Research Fund of Guangxi Key Lab of Multi-source Information Mining & Security (No. 20-A-01-01); the Research Fund of Guangxi Key Lab of Multi-source Information Mining & Security (MIMS20-M-01), the Innovation Project of Guangxi Graduate Education (No. YCSW2021095, No. JXXYYJSCXXM-2021-010).

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 64.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 84.99
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

References

  1. Chen, X., Hong, W., Nie, F., Huang, J.Z., Shen, L.: Enhanced balanced min cut. Int. J. Comput. Vis. 128(7), 1982–1995 (2020)

    Article  MathSciNet  Google Scholar 

  2. Chen, X., Zhexue Haung, J., Nie, F., Chen, R., Wu, Q.: A self-balanced min-cut algorithm for image clustering. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2061–2069 (2017)

    Google Scholar 

  3. Du, T., Wen, G., Cai, Z., Zheng, W., Tan, M., Li, Y.: Spectral clustering algorithm combining local covariance matrix with normalization. Neural Comput. Appl. 32(11), 6611–6618 (2018). https://doi.org/10.1007/s00521-018-3852-z

    Article  Google Scholar 

  4. Gan, J., Peng, Z., Zhu, X., Hu, R., Ma, J., Wu, G.: Brain functional connectivity analysis based on multi-graph fusion. Med. Image Anal. 71, 102057 (2021)

    Article  Google Scholar 

  5. Guo, Y., Wu, Z., Shen, D.: Learning longitudinal classification-regression model for infant hippocampus segmentation. Neurocomputing 391, 191–198 (2020)

    Article  Google Scholar 

  6. Hartigan, J.A., Wong, M.A.: Algorithm as 136:a k-means clustering algorithm. J. Roy. Stat. Soc. Ser. C (Appl. Stat.) 28(1), 100–108 (1979)

    MATH  Google Scholar 

  7. Hu, R., et al.: Multi-band brain network analysis for functional neuroimaging biomarker identification. IEEE Trans. Med. Imaging 40, 3843–3855 (2021)

    Article  Google Scholar 

  8. Hu, R., Zhu, X., Zhu, Y., Gan, J.: Robust SVM with adaptive graph learning. World Wide Web 23(3), 1945–1968 (2020)

    Article  Google Scholar 

  9. Huang, D., Wang, C.D., Wu, J.S., Lai, J.H., Kwoh, C.K.: Ultra-scalable spectral clustering and ensemble clustering. IEEE Trans. Knowl. Data Eng. 32(6), 1212–1226 (2019)

    Article  Google Scholar 

  10. Jain, A.K., Dubes, R.C.: Algorithms for Clustering Data. Prentice-Hall Inc., Upper Saddle River (1988)

    MATH  Google Scholar 

  11. Kang, Z., et al.: Partition level multiview subspace clustering. Neural Netw. 122, 279–288 (2020)

    Article  Google Scholar 

  12. Li, Z., Nie, F., Chang, X., Ma, Z., Yang, Y.: Balanced clustering via exclusive lasso: A pragmatic approach. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)

    Google Scholar 

  13. Liu, H., Han, J., Nie, F., Li, X.: Balanced clustering with least square regression. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)

    Google Scholar 

  14. Lucińska, M.: A spectral clustering algorithm based on eigenvector localization. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2014. LNCS (LNAI), vol. 8468, pp. 749–759. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07176-3_65

    Chapter  Google Scholar 

  15. Luo, Q., Wen, G., Zhang, L., Zhan, M.: An efficient algorithm combining spectral clustering with feature selection. Neural Process. Lett. 52(3), 1913–1925 (2020)

    Article  Google Scholar 

  16. Ng, A.Y., Jordan, M.I., Weiss, Y.: On spectral clustering: analysis and an algorithm. In: Advances in Neural Information Processing Systems, pp. 849–856 (2002)

    Google Scholar 

  17. Nie, F., Wang, C.L., Li, X.: K-multiple-means: a multiple-means clustering method with specified k clusters. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 959–967 (2019)

    Google Scholar 

  18. Nie, F., Wang, X., Jordan, M.I., Huang, H.: The constrained Laplacian rank algorithm for graph-based clustering. In: AAAI, pp. 1969–1976. Citeseer (2016)

    Google Scholar 

  19. Nie, F., Zhang, R., Li, X.: A generalized power iteration method for solving quadratic problem on the Stiefel manifold. Sci. China Inf. Sci. 60(11), 112101 (2017)

    Article  MathSciNet  Google Scholar 

  20. Shen, H.T., et al.: Heterogeneous data fusion for predicting mild cognitive impairment conversion. Inf. Fusion 66, 54–63 (2021)

    Article  Google Scholar 

  21. Shen, H.T., Zhu, Y., Zheng, W., Zhu, X.: Half-quadratic minimization for unsupervised feature selection on incomplete data. IEEE Trans. Neural Netw. Learn. Syst. 32, 3122–3135 (2020)

    Article  MathSciNet  Google Scholar 

  22. Von Luxburg, U.: A tutorial on spectral clustering. Stat. Comput. 17(4), 395–416 (2007)

    Article  MathSciNet  Google Scholar 

  23. Xie, G., et al.: SRSC: selective, robust, and supervised constrained feature representation for image classification. IEEE Trans. Neural Networks Learn. Syst. 31(10), 4290–4302 (2020)

    Article  MathSciNet  Google Scholar 

  24. Yang, Y., Duan, Y., Wang, X., Huang, Z., Xie, N., Shen, H.T.: Hierarchical multi-clue modelling for poi popularity prediction with heterogeneous tourist information. IEEE Trans. Knowl. Data Eng. 31(4), 757–768 (2018)

    Article  Google Scholar 

  25. Yuan, C., Zhong, Z., Lei, C., Zhu, X., Hu, R.: Adaptive reverse graph learning for robust subspace learning. Inf. Process. Manage. 58(6), 102733 (2021)

    Article  Google Scholar 

  26. Zhang, S., Li, X., Zong, M., Zhu, X., Cheng, D.: Learning K for KNN classification. ACM Trans. Intell. Syst. Technol. (TIST) 8(3), 1–19 (2017)

    Google Scholar 

  27. Zhang, Y., Zhao, Q., Jin, J., Wang, X., Cichocki, A.: A novel BCI based on ERP components sensitive to configural processing of human faces. J. Neural Eng. 9(2), 26018 (2012)

    Article  Google Scholar 

  28. Zhang, Z., Liu, L., Shen, F., Shen, H.T., Shao, L.: Binary multi-view clustering. IEEE Trans. Pattern Anal. Mach. Intell. 41(7), 1774–1782 (2018)

    Article  Google Scholar 

  29. Zhou, Y., Tian, L., Zhu, C., Jin, X., Sun, Y.: Video coding optimization for virtual reality 360-degree source. IEEE J. Sel. Top. Sig. Process. 14(1), 118–129 (2019)

    Article  Google Scholar 

  30. Zhu, X., Gan, J., Lu, G., Li, J., Zhang, S.: Spectral clustering via half-quadratic optimization. World Wide Web 23(3), 1969–1988 (2019). https://doi.org/10.1007/s11280-019-00731-8

    Article  Google Scholar 

  31. Zhu, X., Li, X., Zhang, S.: Block-row sparse multiview multilabel learning for image classification. IEEE Trans. Cybern. 46(2), 450–461 (2016)

    Article  Google Scholar 

  32. Zhu, X., Li, X., Zhang, S., Ju, C., Wu, X.: Robust joint graph sparse coding for unsupervised spectral feature selection. IEEE Trans. Neural Netw. Learn. Syst. 28(6), 1263–1275 (2017)

    Article  MathSciNet  Google Scholar 

  33. Zhu, X., Li, X., Zhang, S., Xu, Z., Yu, L., Wang, C.: Graph PCA hashing for similarity search. IEEE Trans. Multimedia 19(9), 2033–2044 (2017)

    Article  Google Scholar 

  34. Zhu, X., et al.: Joint prediction and time estimation of COVID-19 developing severe symptoms using chest CT scan. Med. Image Anal. 67, 101824 (2021)

    Article  Google Scholar 

  35. Zhu, X., Yang, J., Zhang, C., Zhang, S.: Efficient utilization of missing data in cost-sensitive learning. IEEE Trans. Knowl. Data Eng. 33, 2425–2436 (2019)

    Article  Google Scholar 

  36. Zhu, X., Zhang, S., Li, Y., Zhang, J., Yang, L., Fang, Y.: Low-rank sparse subspace for spectral clustering. IEEE Trans. Knowl. Data Eng. 31(8), 1532–1543 (2018)

    Article  Google Scholar 

  37. Zhu, X., Zhang, S., Zhu, Y., Zhu, P., Gao, Y.: Unsupervised spectral feature selection with dynamic hyper-graph learning. IEEE Trans. Knowl. Data Eng., 1 (2020)

    Google Scholar 

  38. Zhu, X., Zhu, Y., Zheng, W.: Spectral rotation for deep one-step clustering. Pattern Recogn. 105, 107175 (2020)

    Article  Google Scholar 

  39. Zhu, Y., Ma, J., Yuan, C., Zhu, X.: Interpretable learning based dynamic graph convolutional networks for Alzheimer’s disease analysis. Inf. Fusion 77, 53–61 (2022)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Guangxi Key Lab of Multi-Source Information Mining and Security, Guangxi Normal University, Guilin, 541004, Guangxi, China

    Qimin Luo, Guangquan Lu, Guoqiu Wen, Zidong Su & Jian Wei

  2. School of Computer Science and Engineering, Guangxi Normal University, Guilin, 541004, Guangxi, China

    Qimin Luo, Guangquan Lu, Guoqiu Wen, Zidong Su & Jian Wei

  3. Guangxi Vocational and Technical Institute of Industry, Nanning, 530001, China

    Xingyi Liu

Authors
  1. Qimin Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guangquan Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Guoqiu Wen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zidong Su
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xingyi Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jian Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Bohan Li

  2. University of Queensland, Brisbane, QLD, Australia

    Lin Yue

  3. University of Technology Sydney, Sydney, NSW, Australia

    Jing Jiang

  4. University of Queensland, Brisbane, QLD, Australia

    Weitong Chen

  5. University of Queensland, Brisbane, QLD, Australia

    Xue Li

  6. University of Technology Sydney, Sydney, NSW, Australia

    Guodong Long

  7. Carnegie Mellon University, Pittsburgh, USA

    Fei Fang

  8. Nanyang Technological University, Singapore, Singapore

    Han Yu

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Luo, Q., Lu, G., Wen, G., Su, Z., Liu, X., Wei, J. (2022). Balanced Spectral Clustering Algorithm Based on Feature Selection. In: Li, B., et al. Advanced Data Mining and Applications. ADMA 2022. Lecture Notes in Computer Science(), vol 13088. Springer, Cham. https://doi.org/10.1007/978-3-030-95408-6_27

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-95408-6_27

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-95407-9

  • Online ISBN: 978-3-030-95408-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation