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A retrieval method of learners’ behavior features based on K-means clustering algorithm

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Abstract

This paper studies the retrieval method of learners’ behavior features in the coding library the K-means clustering algorithm, which can effectively retrieve learners’ behavior features and ensure the safety and stability of the coding library. After constructing the medical coding database, using the missing forest algorithm to fill the missing data in the learner behavior data of the coding database, the improved binary K-means clustering algorithm is used, without setting the number of clusters, it is only necessary to carry out binary clustering operation on the behavior data of learners in the coding library after missing and filling, so as to obtain the behavior characteristics of learners in the coding library; These features are input as a support vector machine classifier. Through the classification training of support vector machine, the learner behavior features in the coding library can be classified and the corresponding retrieval results can be output. The experimental results show that this method can effectively retrieve learners’ behavior features in the code library and identify abnormal behaviors. The retrieval accuracy and efficiency are high, and it is less affected by the signal-to-noise ratio and the amount of data. It has significant advantages in the actual learners’ behavior features retrieval in the code library.

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References

  1. Cai, B., Shen, Y., Wu, J., Yang, Y., Wang, K.: Optimized phase-coding method for absolute phase retrieval based on k-means algorithm. J. Mod. Opt. 68(6), 1–8 (2021)

    Article  MathSciNet  Google Scholar 

  2. Chen, X., Dinavahi, V.: Group behavior pattern recognition algorithm based on spatio-temporal graph convolutional networks. Sci. Program. 2021(1), 1–8 (2021)

    Google Scholar 

  3. Yang, H., Yuan, C., Zhang, L., Sun, Y., Maybank, S.J.: Sta-cnn: convolutional spatial-temporal attention learning for action recognition. IEEE Trans. on Image Process. 99, 1–1 (2020)

    Google Scholar 

  4. Majd, M., Safabakhsh, R.: A motion-aware convlstm network for action recognition. Appl. Intell. 49(7), 1–7 (2019)

    Article  Google Scholar 

  5. Jimenez, F., Martinez, C., Marzano, E., Tomas Palma, J., Sanchez, G., Sciavicco, G.: Multi-objective evolutionary feature selection for fuzzy classification. IEEE Trans. on Fuzzy Syst. 5, 1–1 (2019)

    Google Scholar 

  6. Raghunathan, K., Soundarapandian, R.K., Gandomi, A.H., Ramachandran, M., Madda, R.B.: Duo-stage decision: a framework for filling missing values, consistency check, and repair of decision matrices in multicriteria group decision making. IEEE Trans. Eng. Manag. 99, 1–13 (2019)

    Google Scholar 

  7. Qi, X., Guo, H., Wang, W.: A reliable knn filling approach for incomplete interval-valued data. Eng. Appl. Artif. Intell. 100(315), 104175 (2021)

    Article  Google Scholar 

  8. Satyanarayana, K.V., Rao, N.T., Bhattacharyya, D., Hu, Y.C.: Identifying the presence of bacteria on digital images by using asymmetric distribution with k-means clustering algorithm. Multidimension. Syst. Signal Process. 33(2), 301–326 (2021)

    Article  MathSciNet  Google Scholar 

  9. Qin, X., Li, J., Hu, W., Yang, J.: Machine learning k-means clustering algorithm for interpolative separable density fitting to accelerate hybrid functional calculations with numerical atomic orbitals. J. Phys. Chem. A 124(48), 10066–10074 (2020)

    Article  Google Scholar 

  10. Forster, D., Inderka, R.B., Gauterin, F.: Data-driven identification of characteristic real-driving cycles based on k-means clustering and mixed-integer optimization. IEEE Trans. on Vehicular Technol. 99, 1–1 (2019)

    Google Scholar 

  11. Turner, S., Kelvin, L.S., Baldry, I.K., Lisboa, P.J., Liske, J.: Reproducible k-means clustering in galaxy feature data from the gama survey. Mon. Not. R. Astron. Soc. 482(1), 126–150 (2019)

    Article  Google Scholar 

  12. Guo, W., Cai, J., Wang, S.: Unsupervised discriminative feature representation via adversarial auto-encoder. Appl. Intell. 50, 5552 (2020)

    Article  Google Scholar 

  13. Ding, S., Cong, L., Hu, Q., Jia, H., Shi, Z.: A multiway p-spectral clustering algorithm. Knowledge-Based Syst. 164, 371–377 (2019)

    Article  Google Scholar 

  14. Hadipour, H., Liu, C., Davis, R., Cardona, S.T., Hu, P.: Deep clustering of small molecules at large-scale via variational autoencoder embedding and k-means. BMC Bioinformatics 23(4), 1–22 (2022)

    Google Scholar 

  15. Ganji, H., Ebadzadeh, M.M., Khadivi, S.: Kernel compositional embedding and its application in linguistic structured data classification. Knowl.-Based Syst. 194(9), 105553 (2020)

    Article  Google Scholar 

  16. Yang, Y., Yeh, H.G., Zhang, W., Lee, C.J., Lowe, C.G.: Feature extraction, selection and k-nearest neighbors algorithm for shark behavior classification based on imbalanced dataset. IEEE Sens. J. 99, 1–1 (2020)

    Google Scholar 

  17. Li, L., Zhu, F., Sun, H., Hu, Y., Jin, D.: Multi-source information fusion and deep-learning-based characteristics measurement for exploring the effects of peer engagement on stock price synchronicity. Information Fusion 69(3), 1–21 (2021)

    Google Scholar 

  18. Yin, H., Wang, J., Lin, J., Han, D., Ying, C., Meng, Q.: A memory-attention hierarchical model for driving-behavior recognition and motion prediction. Int. J. Automot. Technol. 22(4), 895–908 (2021)

    Article  Google Scholar 

  19. Lin, F.C., Ngo, H.H., Dow, C.R., Lam, K.H., Le, H.L.: Student behavior recognition system for the classroom environment based on skeleton pose estimation and person detection. Sensors 21(16), 5314 (2021)

    Article  Google Scholar 

  20. Jia, S., Hui, F., Li, S., Zhao, X., Khattak, A.J.: Long short-term memory and convolutional neural network for abnormal driving behaviour recognition. IET Intel. Transport Syst. 14(5), 306–312 (2020)

    Article  Google Scholar 

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Authors and Affiliations

  1. Ningbo Open University, Ningbo, 315016, Zhejiang, China

    Shaohua Wang

  2. Ningbo University, Ningbo, 315211, Zhejiang, China

    Xiaoxiong Xu

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  1. Shaohua Wang
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  2. Xiaoxiong Xu
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Shaohua Wang and Xiaoxiong Xu wrote the main manuscript text. All authors reviewed the manuscript.

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Correspondence to Shaohua Wang.

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Wang, S., Xu, X. A retrieval method of learners’ behavior features based on K-means clustering algorithm. Cluster Comput 27, 2049–2058 (2024). https://doi.org/10.1007/s10586-023-04077-9

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  • DOI: https://doi.org/10.1007/s10586-023-04077-9

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