Title: Application of improved K-means algorithm in the cultivation of creative music talents under the needs of sustainable development and transformation

Authors: Peng Li; Zeng Fan

Addresses: Institute of Advanced Studies in Humanities and Social Sciences, Beijing Normal University, Zhuhai, Zhuhai, 519087, China ' Institute of Advanced Studies in Humanities and Social Sciences, City University of Macau (RCAE), Macao, 999078, China

Abstract: In order to cultivate innovative personnel who are adapted to the development of university education, this paper proposes a K-means clustering algorithm (K-means) based on noise reduction autoencoder for the cultivation of creative music talents and explores the difficulties in cultivating innovative talents. The results show that the research-designed method outperforms K-means on the performance metrics NMI, AMI and FMI for the same dataset. The results of the practical application analysis show that the training of practical operation is weakened in talent training, and the emphasis on practical courses should be strengthened in the subsequent talent training plan.

Keywords: sustainable development; autoencoder; K-means; talent training.

DOI: 10.1504/IJWET.2024.138098

International Journal of Web Engineering and Technology, 2024 Vol.19 No.1, pp.4 - 19

Received: 05 Jan 2023
Received in revised form: 10 Aug 2023
Accepted: 11 Oct 2023
Published online: 29 Apr 2024 *

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