Skip to main content
SpringerLink
Log in

A Novel Fuzzy Distance-Based Minimum Spanning Tree Clustering Algorithm for Face Detection

  • Published:
Cognitive Computation Aims and scope Submit manuscript

Abstract

Solving a clustering algorithm can usually be simplified into an optimization problem. Using relevant knowledge in graph theory, many optimization problems can be transformed into solving minimum spanning tree problems. Minimal spanning trees are also widely used in areas closely related to cognitive computing such as for face recognition by face cognition and gene data analysis by gene cognition. However, the minimum spanning tree has the shortcoming of the distance between neighbours because of which the minimum spanning tree algorithm cannot cluster unbalanced data. Thus, the face recognition rate is low, and facial expression cognition is difficult. In this paper, a minimum spanning tree algorithm based on fuzzy distance is proposed for the shortcomings of the minimum spanning tree (FCP). First, a relative neighbourhood distance measure is proposed by introducing neighbourhood rough set theory; the neighbourhood matrix is obtained based on the distance. Second, the minimum spanning tree is solved by the prim algorithm and the neighbourhood matrix. Finally, the minimum spanning tree is partitioned to realize clustering of the minimum spanning tree. In this paper, the UCI dataset and Olivetti face database are selected to verify the performance of the algorithm, and the algorithm is evaluated by three evaluation criteria. The experimental results show that the proposed algorithm can not only cluster data of any shape but also deal with unbalanced data containing noise points. Especially in face cognitive computing, the values of ACC, AMI, and ARI can reach 0.852, 0.843, and 0.782, respectively. In this study, the algorithm can obtain very good clustering results for data with good geometric structure, and the overall performance is better than other algorithms. In face recognition detection, the improved cognitive computing of faces makes it possible to accurately recognize different expressions from the same person.

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
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

References

  1. Fan J, Niu Z, Liang Y, et al. Probability model selection and parameter evolutionary estimation for clustering imbalanced data without sampling. Neurocomputing. 2016;211:172–81.

    Article  Google Scholar 

  2. Moore A W. Very fast EM-based mixture model clustering using multiresolution Kd-trees. Neural Information Processing systems. 2020; 543–549.

  3. Zahn CT. Graph-theoretical methods for detecting and describing gestalt clusters. IEEE Trans Comput. 2019;20(1):68–86.

    Article  Google Scholar 

  4. Doucet, Arnaud, Freitas, Nando, Gordon, Neil. Sequential Monte Carlo methods in practice || an introduction to sequential monte carlo methods. 2020, 1(1):3–14.

  5. Zhong C, Miao D, Wang R. A graph-theoretical clustering method based on two rounds of minimum spanning trees. Pattern Recogn. 2010;43(3):752–66.

    Article  Google Scholar 

  6. Rhouma MB, Frigui H. Self-organization of pulse-coupled oscillators with application to clustering. IEEE Trans Pattern Anal Mach Intell. 2001;23(2):180–95.

    Article  Google Scholar 

  7. Han J. Data mining: concepts and techniques. 2005.

  8. Fox WR. Finding groups in data: an introduction to cluster analysis. J R Stat Soc: Ser C: Appl Stat. 2020;40(3):486–7.

    Google Scholar 

  9. Ng RT, Han J. CLARANS: a method for clustering objects for spatial data mining. IEEE Trans Knowl Data Eng. 2018;14(5):1003–16.

    Article  Google Scholar 

  10. Hinneburg A. An efficient approach to clustering in large multimedia databases with noise. Knowledge Discovery and Data Mining (KDD '98), 1998; 13(4):332–344.

  11. Ankerst M, Breunig M M, Kriegel H P, et al. OPTICS: ordering points to identify the clustering structure. SIGMOD 1999, Proceedings ACM SIGMOD International Conference on Management of Data, June 1–3, 1999, Philadelphia, Pennsylvania, USA. ACM. 1999.

  12. Zhang T, Ramakrishnan R, Livny M, et al. BIRCH: an efficient data clustering method for very large databases. International Conference on Management of Data. 1996;25(2):103–14.

    Google Scholar 

  13. Guha S, Rastogi R, Shim K, et al. Cure: an efficient clustering algorithm for large databases. Inf Syst. 2001;26(1):35–58.

    Article  Google Scholar 

  14. Sinaga KP, Yang M. Unsupervised K-means clustering algorithm. IEEE Access. 2020;8:80716–27.

    Article  Google Scholar 

  15. Lloyd SP. Least squares quantization in PCM. IEEE Trans Inf Theory. 1982;28(2):129–37.

    Article  MathSciNet  Google Scholar 

  16. Li R, Wang S, Gu D. Ongoing evolution of visual SLAM from geometry to deep learning: challenges and opportunities. Cogn Comput. 2018;10:875–89.

    Article  Google Scholar 

  17. Xie J, Jiang W, Ding L. Clustering by searching density peaks via local standard deviation. International Conference on Intelligent Data Engineering and Automated Learning. Springer, Cham, 2017; 4:295–305.

  18. Yang Xu, Deng C, Wei K, Yan J, Liu W. Adversarial learning for robust deep clustering. NeurIPS Proceedings. 2020;10(4):112–9.

    Google Scholar 

  19. RuhuiLiu WeipingHuang, ZhengshunFei KaiWang, JunLiang,. Constraint-based clustering by fast search and find of density peaks. Neurocomputing. 2018;319:1–196.

    Article  Google Scholar 

  20. Ankerst M, Breunig MM, Kriegel HP, et al. OPTICS: ordering points to identify the clustering structure. Proceeding of the ACM SIGMOD International Conference on Management of Data. Philadelphia. 2020; 6:49–60.

  21. Frey BJ, Eueck D. Clustering by passing messages between data points. Science. 2007;315(5814):972–6.

    Article  MathSciNet  Google Scholar 

  22. Bie R, Mehmood R, Ruan S, et al. Adaptive fuzzy clustering by fast search and find of density peaks. Pers Ubiquit Comput. 2016;20(5):785–93.

    Article  Google Scholar 

  23. Rodriguez A, Laio A. Clustering by fast search and find of density peaks. Science. 2020;344(6191):1492–6.

    Article  Google Scholar 

  24. Xie J, Gao H, Xie W, et al. Robust clustering by detecting density peaks and assigning points based on fuzzy weighted K-nearest neighbors. Inf Sci. 2019;354:19–40.

    Article  Google Scholar 

  25. Xie JY, Gao HC, Xie WX. K-nearest neighbors optimized clustering algorithm by fast search and finding the density peaks of a dataset. Science Chia: Inform Sci. 2016; 46(2):258–280.

  26. Weihua H, Takeru M, Seiya T, Eiichi M, Masashi S. Learning discrete representations via information maximizing self-augmented training. In Proceedings of the 34th International Conference on Machine Learning (ICML'17). 2017; 1558–1567.

  27. Chang J, Wang L, Meng G, Xiang S, Pan C. Deep adaptive image clustering. IEEE International Conference on Computer Vision (ICCV). 2017;2017:5880–8.

    Article  Google Scholar 

  28. Kumar N, Gumhold S. FuseVis: interpreting neural networks for image fusion using per-pixel saliency visualization. Computers. 2020;9:98.

    Article  Google Scholar 

  29. Fahad A, Alshatri N, Tari Z, et al. A survey of clustering algorithms for big data: taxonomy and empirical analysis. IEEE Trans Emerg Top Comput. 2014;2(3):267–79.

    Article  Google Scholar 

  30. Xu Y, Olman V, Xu D, et al. Clustering gene expression data using a graph-theoretic approach: an application of minimum spanning trees. Bioinformatics. 2020;18(4):536–45.

    Article  Google Scholar 

  31. Wang X, Wang XL, Chen C, et al. Enhancing minimum spanning tree-based clustering by removing density-based outliers. Digital Signal Process. 2019;23(5):1523–38.

    Article  MathSciNet  Google Scholar 

  32. Jiang J, Chen Y, Meng X, et al. A novel density peaks clustering algorithm based on k nearest neighbors for improving assignment process. Physica A. 2019;523:702–13.

    Article  Google Scholar 

  33. Parmar MD, Pang W, Hao D, et al. FREDPC: a feasible residual error-based density peak clustering algorithm with the fragment merging strategy. IEEE Access. 2019;6(99):1–7.

    Google Scholar 

  34. Rodriguez A, Laio A. Clustering by fast search and find of density peaks. Science. 2014;344(6191):1492–6.

    Article  Google Scholar 

  35. Yan H, Wang L, Lu Y. Identifying cluster centroids from decision graph automatically using a statistical outlier detection method. Neurocomputing. 2019; 329(FEB.15):348–358.

Download references

Acknowledgements

This research is financially supported by The National Natural Science Foundation of China (61877065).

Author information

Authors and Affiliations

  1. School of Mechanical and Electrical Engineering and Automation, Shanghai University, Shanghai, 200444, China

    Yang Li & Wenju Zhou

Authors
  1. Yang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wenju Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wenju Zhou.

Ethics declarations

Ethics Approval

This article does not contain any experiments with human or animal participants performed by any of the authors.

Consent to Participate

Informed consent was obtained from all individual participants included in the study.

Conflict of Interest

The authors declare no competing interests.

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

Li, Y., Zhou, W. A Novel Fuzzy Distance-Based Minimum Spanning Tree Clustering Algorithm for Face Detection. Cogn Comput 14, 1350–1361 (2022). https://doi.org/10.1007/s12559-022-10002-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12559-022-10002-w

Keywords

Search

Navigation