Skip to main content
Springer Nature Link
Log in

A new semi-supervised fuzzy K-means clustering method with dynamic adjustment and label discrimination

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Conventional unsupervised Fuzzy K-means methods (FKM) usually analyze the structure of data solely without considering the influence of label information carried by data, which limits the performance and stability of clustering. How to leverage annotated label information to improve the performance of unsupervised FKM methods is still a challenging research problem. To that end, this paper proposes a new Semi-Supervised Fuzzy K-means method (SSFKM) consisting of dynamic adjustment and label discrimination. Specifically, dynamic adjustment aligns label information and clustering results to distinguish the learning difficulties of labeled data and enable the method to focus on simple but reliable label information. Moreover, a new distance measure is designed to re-evaluate the membership of labeled data with cluster centers, forcing labeled data to be classified into correct cluster for enhancing label discrimination. Comprehensive experiments demonstrate that the SSFKM method achieves the best performance compared with existing state-of-the-art semi-supervised clustering methods. In addition, the results demonstrate that the SSFKM method reduces the impact of data noise effectively during clustering.

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
$34.99 /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 excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Algorithm 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Data availability

The datasets used in this paper are publicly available.

References

  1. Sulaiman SN, Isa NAM (2011) Adaptive fuzzy-K-means clustering algorithm for image segmentation. IEEE Trans Consum Electron 56(4):2661–2668

    Article  Google Scholar 

  2. Thaipanich T, Oh BT, Wu PH, Xu DR (2011) Improved image denoising with adaptive nonlocal means (ANL-Means) algorithm. IEEE Trans Consum Electron 56(4):2623–2630

    Article  Google Scholar 

  3. Shanthi I, Valarmathi ML (2013) SAR image despeckling using possibilistic fuzzy C-means clustering and edge detection in bandelet domain. Neural Comput Appl 23(1):279-S291

    Article  Google Scholar 

  4. Hao T, Rusanov A, Boland M et al (2014) Clustering clinical trials with similar eligibility criteria features. J Biomed Inform 52(c):112–120

    Article  PubMed  PubMed Central  Google Scholar 

  5. Xu C, Tao D, Xu C (2015) Multi-view intact space learning. IEEE Trans Pattern Anal Mach Intell 37(12):2531–2544

    Article  PubMed  Google Scholar 

  6. Zhou B, Liu W, Zhang W et al (2022) Multi-kernel graph fusion for spectral clustering. Inf Process Manage 59(5):103003

    Article  Google Scholar 

  7. Choi M, Chang IJ, Kim J (2016) Optimal reference view selection algorithm for low complexity disparity estimation. IEEE Trans Consum Electron 62(1):45–52

    Article  Google Scholar 

  8. McQueen J (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of the 5th Berkeley Symposium on Mathematics Statistics and Probability pp 281–297

  9. Ruspini E (1969) A new approach to clustering. Inf Control 15(1):22–32

    Article  Google Scholar 

  10. Bezdek J (1981) Pattern recognition with fuzzy objective function algorithms. Plenum Press, London

    Book  Google Scholar 

  11. Xu J, Han J, Kai X, et al (2016) Robust and sparse fuzzy k-means clustering. In: Proceedings of the 25th International Joint Conference on Artificial Intelligence, pp 2224–2230

  12. Bui Q, Vo B, Snasel V et al (2020) SFCM: a fuzzy clustering algorithm of extracting the shape information of data. IEEE Trans Fuzzy Syst 29(1):75–89

    Article  Google Scholar 

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

  14. Zhou J, Pedrycz W, Yue X et al (2022) Projected fuzzy C-means clustering with locality preservation. Pattern Recogn 113(6):107748

    Google Scholar 

  15. Guo Y, Sengur A (2015) NCM: neutrosophic c-means clustering algorithm. Pattern Recogn 48(8):2710–2714

    Article  ADS  Google Scholar 

  16. Guo Y, Sengur A (2015) NECM: neutrosophic evidential c-means clustering algorithm. Neural Comput Appl 26(3):561–571

    Article  Google Scholar 

  17. Akbulut Y, Abdulkadir S, Guo Y et al (2017) KNCM: kernel neutrosophic c-means clustering. Appl Soft Comput 52:714–724

    Article  Google Scholar 

  18. Xi L, Zhang FB (2020) An adaptive artificial-fish-swarm-inspired fuzzy C-means algorithm. Neural Comput Appl 32(22):16891–16899

    Article  Google Scholar 

  19. Nguyen TPQ, Kuo RJ, Le MD et al (2022) Local search genetic algorithm-based possibilistic weighted fuzzy c-means for clustering mixed numerical and categorical data. Neural Comput Appl 34(20):18059–18074

    Article  Google Scholar 

  20. Grira N, Crucianu M, Boujemaa N (2008) Active semi-supervised fuzzy clustering. Pattern Recogn 41(5):1834–1844

    Article  ADS  Google Scholar 

  21. Zhang H, Jing L (2009) Semi-supervised fuzzy clustering: a kernel-based approach. Knowl-Based Syst 22(6):477–481

    Article  Google Scholar 

  22. Zhang R, Nie F, Guo M et al (2018) Joint learning of fuzzy k-means and nonnegative spectral clustering with side information. IEEE Trans Image Process 28(5):2152–2162

    Article  ADS  MathSciNet  PubMed  Google Scholar 

  23. Li L, Garibaldi JM, He D et al (2015) Semi-supervised fuzzy clustering with feature discrimination. PLoS ONE 10(9):131–160

    Google Scholar 

  24. Zhang D, Ma Y, Zhu H et al (2022) A label-guided weighted semi-supervised neutrosophic clustering algorithm. J Intell Fuzzy Syst 43(5):5661–5672

    Article  Google Scholar 

  25. Shi C, Gu Z, Duan C et al (2020) Multi-view adaptive semi-supervised feature selection with the self-paced learning. Signal Process 168:107332

    Article  Google Scholar 

  26. Chen L, Lu J (2021) Adaptive graph learning for semi-supervised self-paced classification. Neural Process Lett 54(4):2695–2716

    Article  Google Scholar 

  27. Basu S, Banerjee A, Mooney R (2002) Semi-supervised clustering by seeding. In: Proceedings of the 19th International Conference on Machine Learning, pp 19–26

  28. Wagstaff K, Cardie C (2000) Clustering with instance level constraints. In: Proceedings of the 17th International Conference on Machine Learning, pp 1097–1103

  29. Wagstaff K, Cardie C, Rogerss S, et al (2001) Constrained K-means clustering with background knowledge. In Proceedings of the 18th International Conference on Machine Learning, pp 577–584

  30. Nie F, Cai G, Li X (2017) Multi-view clustering and semi-supervised classification with adaptive neighbours. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence, pp 2415–2421

  31. Zhuang L, Zhou Z, Gao S et al (2017) Label information guided graph construction for semi-supervised learning. IEEE Trans Image Process 26(9):4182–4192

    Article  ADS  MathSciNet  PubMed  Google Scholar 

  32. Chen L, Zhong Z (2022) Adaptive and structured graph learning for semi-supervised clustering. Inf Process Manage 59(4):102949

    Article  Google Scholar 

  33. Wang D, Nie F, Huang H (2014) Large-scale adaptive semi-supervised learning via unified inductive and transductive model. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp 482–491

  34. Nie F, Hua W, Huang H, et al (2013) Adaptive loss minimization for semi-supervised elastic embedding. In: Proceedings of the 23th International Joint Conference on Artificial Intelligence, pp 1565–1571

  35. Qiu S, Nie F, Xu X et al (2018) Accelerating flexible manifold embedding for scalable semi-supervised learning. IEEE Trans Circuits Syst Video Technol 29(9):2286–2295

    Google Scholar 

  36. Chang X, Nie F, Yang Y, et a1 (2014) A convex formulation for semi-supervised multi-label feature selection. In: Proceedings of the 28th AAAI Conference on Artificial Intelligence, pp 1171–1177

  37. Ding S, Jia H, Du M et al (2018) A semi-supervised approximate spectral clustering algorithm based on HMRF model. Inf Sci 429:215–228

    Article  MathSciNet  Google Scholar 

  38. Nie F, Wang X, Jordan M I, et al (2016) The constrained laplacian rank algorithm for graph-based clustering. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence, pp 1969–1976

  39. Strehl A, Ghosh J (2002) Cluster ensembles-a knowledge reuse framework for combining multiple partitions. J Mach Learn Res 3(3):583–617

    MathSciNet  Google Scholar 

  40. William M (1971) Objective criteria for the evaluation of clustering methods. J Am Stat Assoc 66(336):846–850

    Article  Google Scholar 

  41. Demiar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1–30

    MathSciNet  Google Scholar 

Download references

Acknowledgements

This work was supported in part by the National Social Science Fund of China (18ZDA153, 19BFX127, 19BYY125), in part by the Research Grants Council of the Hong Kong Special Administrative Region, China (UGC/FDS16/E09/22), and in part by the Natural Science Foundation of Guangdong Province (2021A1515011339).

Author information

Authors and Affiliations

  1. School of Computer Science, South China Normal University, Guangzhou, 510000, China

    Hengdong Zhu & Tianyong Hao

  2. Department of Computer Science, City University of Hong Kong, Hong Kong, 999077, China

    Wenxiu Xie

  3. School of Foreign Languages, Chaohu University, Hefei, 238000, China

    Yuanyuan Mu

  4. School of Big Data and Law & School of Law and Public Affairs, Nanjing University of Information Science and Technology, Nanjing, 210000, China

    Juan Xu

  5. School of Science and Technology, Hong Kong Metropolitan University, Hong Kong, 999077, China

    Fu Lee Wang

  6. School of Business, Guangdong University of Foreign Studies, Guangzhou, 510000, China

    Yingying Qu

Authors
  1. Hengdong Zhu
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Wenxiu Xie
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Yuanyuan Mu
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Juan Xu
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Fu Lee Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Yingying Qu
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Tianyong Hao
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding authors

Correspondence to Yingying Qu or Tianyong Hao.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article.

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

Zhu, H., Xie, W., Mu, Y. et al. A new semi-supervised fuzzy K-means clustering method with dynamic adjustment and label discrimination. Neural Comput & Applic 36, 4709–4725 (2024). https://doi.org/10.1007/s00521-023-09115-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-023-09115-6

Keywords