Skip to main content
SpringerLink
Log in

An improved FCM clustering algorithm with adaptive weights based on PSO-TVAC algorithm

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Fuzzy c-means clustering algorithm (FCM), as the most widely used clustering algorithm, works by iteratively updating the membership degree and the cluster centers to improve the effectiveness of clustering. The performance of FCM algorithm is chiefly evaluated by intra-cluster compactness and inter-cluster separation. However, it has some defects such as high dependency on the initial cluster centers, sensitivity to the noise samples and outliers, difficulties in obtaining the optimization of hyperparameters, a fairly poor performance on datasets with the nonuniform distribution. The main purpose of this paper is to tackle these issues. The novelty of this paper is three-fold: 1) a new FCM clustering algorithm (i.e., CWAFCM) has been proposed, which has a good capability of performing the clustering on datasets with nonuniform distribution and reducing the high dependency on the initial cluster centers; 2) considering the merits of AFCM-SP in removing noise samples and CWAFCM in performing clustering on datasets with nonuniform distribution, a combination of the objective functions of these two clustering algorithms is developed to construct the hybrid AFCM algorithm; and 3) during the parameter setting by means of the PSO algorithm with time-varying acceleration coefficients (PSO-TVAC), a new index, namely adaptive clustering validity index (ACVI), is presented to describe the intra-cluster compactness and the inter-cluster separation in a proper manner. Experiments on six data sets in UCI and one artificial data set have been carried out with a comparison of five well-known FCM algorithms. Experimental results have demonstrated that the proposed hybrid AFCM with adaptive weights can more effectively enhance the performance of FCM to increase the clustering effectiveness than the contrastive algorithms. The ranks for seven algorithms on seven datasets in terms of CVIXB, accuracy, and normalized mutual information(NMI), further verifying the superiority of the new algorithm. Therefore it can be concluded that the proposed hybrid AFCM algorithm performs well in reducing the reliance on the initial cluster centers and the sensitivity to the noise and outliers.

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

Similar content being viewed by others

References

  1. Dunn JC (1973) A fuzzy relative of the ISODATA process and its use in detecting compact Well-Separated clusters. J Cybern 3(3):32–57

    Article  MathSciNet  Google Scholar 

  2. Bezdek JC. (1981) Pattern recognition with fuzzy objective function algorithms. Plenum Press

  3. Kesemen O (2016) Fuzzy c-means clustering algorithm for directional data (FCM4DD). Pergamon Press Inc

  4. Goktepe AB, Altun S (2005) Sezer, A.Soil clustering by fuzzy c-means algorithm. Adv Eng Softw 36(10):691–698

    Article  Google Scholar 

  5. Lei Z, Wang L, Wang X, Liu K, Abraham A (2012) Research of neural network classifier based on FCM and PSO for breast cancer classification. Hybrid Artif Intell Syst 7206:647–654

    Google Scholar 

  6. Kari T, Wensheng G (2017) Power transformer fault diagnosis using FCM and improved PCA. J Eng 2017(14):2605–2608

    Article  Google Scholar 

  7. Chen R, Fengbin Z, Liang X (2019) Anomaly Detection Algorithm Based on FCM with improved Krill Herd. J Phys Conf 1187(4):042028

    Article  Google Scholar 

  8. Huang H, Fanzhi M, Shaohua Z, Feng J, Gunasekaran M (2019) Brain image segmentation based on FCM clustering algorithm and rough set. IEEE Access 7:12386–12396

    Article  Google Scholar 

  9. Yang J, Yun-Sheng KE, Mao Zheng W (2017) An adaptive clustering segmentation algorithm based on FCM. Turkish J Electric Eng Comput Sci 25(6):4533–4544

    Article  Google Scholar 

  10. Kannan SR, Devi R, Ramathilagam S, Takezawa K, Effective FCM (2013) Noise clustering algorithms in medical images. Comput Biol Med 43(2):73–83

    Article  Google Scholar 

  11. Qamar U (2014) A dissimilarity measure based Fuzzy c-means (FCM) clustering algorithm. J Intell Fuzzy Syst 26(1):229–238

    Article  MathSciNet  Google Scholar 

  12. Ayan S, Karlekar A, Krejcar O et al (2020) Fuzzy c-means clustering using Jeffreys-divergence based similarity measure. Appl Soft Comput 88

  13. Minxuan L (2019) An improved FCM clustering algorithm based on cosine similarity. ACM International Conference Proceeding Series, Hong Kong, pp 103–109

    Google Scholar 

  14. Kumar N, Kumar H, Sharma K (2020) Extension of FCM by introducing new distance metric. SN Applied Sciences 714(2)

  15. Bezdek JC (1974) Numerical taxonomy with fuzzy sets. J Math Biol 1(1):57–71

    Article  MathSciNet  Google Scholar 

  16. Bezdek James C (1974) Cluster validity with fuzzy sets. J Cybern 3(3):58–73

    Article  MathSciNet  Google Scholar 

  17. Xie XL, Beni G (1991) A validity measure for fuzzy clustering. IEEE Trans Pami 13(8):841–847

    Article  Google Scholar 

  18. Bensaid AM, Hall LO, Bezdek JC, et al. (1996) Validity-guided (re)clustering with applications to image segmentation. IEEE Trans Fuzzy Syst 4(2):112–123

    Article  Google Scholar 

  19. Kwon SH (1998) Cluster validity index for fuzzy clustering. Electronics Letters

  20. Tang Y, Fuchun S, Zengqi S (2005) Improved validation index for fuzzy clustering american control conference

  21. Zhu LF, Wang JS, Wang HY (2019) A novel clustering validity function of FCM clustering algorithm. IEEE Access 7:152289–152315

    Article  Google Scholar 

  22. Lancichinetti A, Fortunato S, Kertész J (2009) Detecting the overlapping and hierarchical community structure in complex networks. New J Phys 11

  23. Munusamy S, Punniyamoorthy M (2020) Modified dynamic fuzzy c-means clustering algorithm application in dynamic customer segmentation. Appl Intell. https://doi.org/10.1007/s10489-019-01626-x

  24. Yang F, Tieli S, Changhai Z (2009) An efficient hybrid data clustering method based on K-harmonic means and particle swarm optimization. Expert Syst Appl 36(6):9847–9852

    Article  Google Scholar 

  25. Hesam I, Ajith A (2011) Fuzzy C-means and fuzzy swarm for fuzzy clustering problem. Expert Syst Appl 38(3):1835–1838

    Article  Google Scholar 

  26. Wu Z, Zhongcheng W, Jun Z (2017) An improved FCM algorithm with adaptive weights based on SA-PSO. Neural Comput Appl (28):3113-3118,2213

  27. Kennedy J, Eberhart R (1995) Particle swarm optimization. Icnn95-international Conference on Neural Networks

  28. Liu W, Zidong W, Xiaohui L, Nianyin Z, David B (2019) A novel particle swarm optimization approach for patient clustering from emergency departments. IEEE Trans Evol Comput 23(04):632–644

    Article  Google Scholar 

  29. Ratnaweera A, Halgamuge SK, Watson HC (2004) Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients. IEEE Trans Evol Comput 8(3):240–255

    Article  Google Scholar 

  30. Zhou KL, Fu C, Yang SL (2014) Fuzziness parameter selection in fuzzy c-means: The perspective of cluster validation. Sci China Inform Sci 57(11):1–8

    Google Scholar 

Download references

Acknowledgements

The authors thank the editor and anonymous reviewers for their detailed and constructive comments that help us to increase the quality of this work. This work was supported in part by the National Natural Science Foundation of China under Grant 61873169, in part by the Natural Science Foundation of Shanghai under Grant 18ZR1427100 and in part by the National Natural Science Foundation of China under Grants 62073223.

Author information

Authors and Affiliations

  1. College Of Science, University of Shanghai for Science and Technology, Shanghai, 200093, China

    Jianhua Hu, Huilin Yin & Guoliang Wei

  2. Department of Control Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China

    Yan Song

Authors
  1. Jianhua Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Huilin Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Guoliang Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yan Song
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jianhua Hu.

Ethics declarations

Conflict of Interests

We declare that no conflict of interest exits in the submission of this manuscript, and the manuscript is approved by all authors for publication.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hu, J., Yin, H., Wei, G. et al. An improved FCM clustering algorithm with adaptive weights based on PSO-TVAC algorithm. Appl Intell 52, 9521–9536 (2022). https://doi.org/10.1007/s10489-021-02801-9

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-021-02801-9

Keywords

Search

Navigation