Skip to main content
SpringerLink
Log in

Neural network in sports cluster analysis

  • Special Issue on Multi-modal Information Learning and Analytics on Big Data
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

In the era of rapid development of the Internet, various types of data in daily life are becoming more and more important, as are people's sports data. How to collect and store these massive exercise data and how to extract the user's exercise habits from these data are of great significance for improving people's exercise enthusiasm. This article mainly studies the application of the network in the cluster analysis of sports. This paper proposes a combination of a neural network-based sports data analysis model and a density peak clustering algorithm for unsupervised dimensionality reduction of high-dimensional data. We design both the encoder and the decoder with a three-layer fully connected neural network structure. The encoder extracts the characteristics of the sample data, and then, the decoder approximates the original input sample. The encoder is used to reduce the dimensionality of the high-dimensional data to the middle dimension, combined with the density peak clustering algorithm to further reduce the dimensionality, and then analyze the low-dimensional data. And set the corresponding learning rate for different data sets, the three data sets are iterated 40 times. The prediction accuracy rates of the algorithm in the three data sets are 94.1%, 90.3%, and 89.6% respectively; compared with the traditional PCA dimensionality reduction method, the method in this paper can extract data features more effectively, improve the dispersion between clusters, and have a better clustering effect. Finally, a numerical example is given to illustrate the effectiveness of the proposed algorithm.

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

Similar content being viewed by others

References

  1. Chandler RE, Juhlin K, Fransson J et al (2017) Current safety concerns with human papillomavirus vaccine: a cluster analysis of reports in vigibase. Drug Saf 40(1):81–90

    Article  Google Scholar 

  2. Chen FC, Jahanshahi RMR (2018) NB-CNN: deep learning-based crack detection using bp neural network and naïve bayes data fusion. IEEE Trans Industr Electron 65(99):4392–4400

    Article  Google Scholar 

  3. Feng X, Qin B, Liu T (2018) A language-independent neural network for event detection ece China. Inf ences 61(09):81–92

    Google Scholar 

  4. Wen L, Li X, Gao L et al (2018) A new BP neural network-based data-driven fault diagnosis method. IEEE Trans Industr Electron 65(99):5990–5998

    Article  Google Scholar 

  5. Yuan Q, Wei Y, Meng X et al (2018) A multiscale and multidepth BP neural network for remote sensing imagery pan-sharpening. IEEE J Select Topics Appl Earth Observ Remote Sens 11(3):978–989

    Article  Google Scholar 

  6. Wang SH, Lv YD, Sui Y et al (2018) Alcoholism detection by data augmentation and BP neural network with stochastic pooling. J Med Syst 42(1):1–11

    Article  Google Scholar 

  7. Chunlei Z, Kazuhito K, Hansen JHL (2018) Text-independent speaker verification based on triplet BP neural network embeddings. IEEE/ACM Trans Audio Speech Language Process 26(9):1633–1644

    Article  Google Scholar 

  8. Weinan E, Wang Q (2018) Exponential convergence of the deep neural network approximation for analytic functions ence. China Math 61(010):1733–1740

    Article  MathSciNet  Google Scholar 

  9. Fister I, Fister D, Deb S et al (2020) Post hoc analysis of sport performance with differential evolution. Neural Comput Appl 32:10799–10808

    Article  Google Scholar 

  10. Zhang L, Luo T, Zhanga F et al (2018) A recommendation model based on deep neural network. IEEE Access 6(99):9454–9463

    Article  Google Scholar 

  11. Wenbin J, Peilin L, Fei W (2018) Speech Magnitude spectrum reconstruction from MFCCs using deep neural network. Chinese J Electron 27(002):393–398

    Article  Google Scholar 

  12. Du L, Du Y, Li Y et al (2018) A reconfigurable streaming deep bp neural network accelerator for internet of things. IEEE Trans Circuits Syst I Regul Pap 65(1):198–208

    Article  MathSciNet  Google Scholar 

  13. Chen J, Liu Z, Wang H et al (2018) Automatic defect detection of fasteners on the catenary support device using deep BP neural network. IEEE Trans Instrum Meas 67(2):257–269

    Article  Google Scholar 

  14. Wang F, Chen B, Lin C et al (2018) Adaptive neural network finite-time output feedback control of quantized nonlinear systems. IEEE Trans Cybern 48(6):1839–1848

    Article  Google Scholar 

  15. Alanis AY (2018) Electricity prices forecasting using artificial neural networks. IEEE Latin Am Trans 16(1):105–111

    Article  MathSciNet  Google Scholar 

  16. Kohl N, Miikkulainen R (2018) Evolving neural networks for strategic decision-making problems. Ann Surg Oncol 22(3):326–337

    Google Scholar 

  17. Fang Y, Zhang C, Yang W et al (2018) Blind visual quality assessment for image super-resolution by BP neural network. Multimed Tools Appl 77(10):1–18

    Google Scholar 

  18. Hu M, Graves CE, Li C et al (2018) Memristor-based analog computation and neural network classification with a dot product engine. Adv Mater 30(9):1–10

    Article  Google Scholar 

  19. Sinyor M, Tan LP, Schaffer A et al (2016) Suicide in the oldest old: an observational study and cluster analysis. Int J Geriatric Psychiat 31(1):33–40

    Article  Google Scholar 

  20. Mahjour SK, Al-Askari MKG, Masihi M (2016) Identification of flow units using methods of Testerman statistical zonation, flow zone index, and cluster analysis in Tabnaak gas field. J Petrol Explor Prod Technol 6(4):577–592

    Article  Google Scholar 

  21. Wildgaard L (2016) A critical cluster analysis of 44 indicators of author-level performance. J Inform 10(4):1055–1078

    Article  Google Scholar 

  22. Glatman-Freedman A, Kaufman Z, Kopel E et al (2016) Near real-time space-time cluster analysis for detection of enteric disease outbreaks in a community setting. J Infect 73(2):99–106

    Article  Google Scholar 

  23. Hall A, Oudyk J, King A et al (2016) Identifying knowledge activism in worker health and safety representation: A cluster analysis. Am J Ind Med 59(1):42–56

    Article  Google Scholar 

  24. Da CDAC, Pelicioni SR (2016) Santana Annaíza Braga Bignardi Cluster analysis of breeding values for milk yield and lactation persistency in Guzerá cattle. Ciencia Rural 46(7):1281–1288

    Article  Google Scholar 

  25. Damas F, Nosaka K, Libardi C et al (2016) Susceptibility to exercise-induced muscle damage: a cluster analysis with a large sample. Int J Sports Med 37(08):633–640

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Sports Institute, Liaocheng University, Liaocheng, 252000, Shandong, China

    Yanhua Zhang

  2. Institute of Sports Training, Nanjing Institute of Sports, Nanjing, 210000, Jiangsu, China

    Xuehua Hou

  3. Department of Physical Education, Xi’an Shiyou University, Xi’an, 710056, Shaanxi, China

    Shan Xu

Authors
  1. Yanhua Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xuehua Hou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shan Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yanhua Zhang.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest regarding the publication of the research article.

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

Zhang, Y., Hou, X. & Xu, S. Neural network in sports cluster analysis. Neural Comput & Applic 34, 3301–3309 (2022). https://doi.org/10.1007/s00521-020-05585-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-020-05585-0

Keywords

Search

Navigation