Deep learning algorithm of clustering analysis in IUR teaching system

• Main Authors: Liu, QI, Gao, Zheng, Zhang, Yun
• Format: Conference Proceeding
• Language: English
• Published: SPIE 10.10.2023
• ISBN: 1510668527; 9781510668522
• ISSN: 0277-786X
• DOI: 10.1117/12.3006116

Optimized data analysis methods can enhance teaching system efficiency. The optimization algorithm has spontaneous recognition of high-dimensional data sparsity in the density peak clustering information. This study proposed a deeplearning algorithm in the density peak clustering analysis of the IUR teaching system, which has the advantages of fast calculation speed and dynamic data update. The research is based on the k-means clustering method to select IUR teaching system loads, and the pheromone parameter is used as an influencing factor in the clustering process. The proposed algorithm can better implement teaching data analysis across systems. In addition, the improved algorithm has low complexity and strong operability. The innovative elements of the IUR teaching systems such as renewable subjects, R&D resource input, and innovative teaching management can be controlled collaboratively. In the experimental analysis, the proposed algorithm can further strengthen the quantitative analysis of the IUR teaching system configuration, improve the refinement method for the effective control and distribution of innovative teaching systems, and fully exploit the data resources for the combination of innovative applications and teaching systems development.