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Bibliometric analysis of research talent evaluation in Chinese universities:data mining approach

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摘要 1 Introduction Existing university research evaluation mechanisms face issues like unclear value orientation,poor classification,suboptimal expert selection,and unscientific organization[1].A comprehensive,scientific evaluation system is essential for advancing higher education.Motivation.Traditional evaluation methods struggle to keep up with the increasing complexity and variety of evaluation indicators[2].Data mining techniques,such as cooccurrence analysis and clustering,have been effective in uncovering hidden patterns in large datasets,addressing the limitations of traditional qualitative methods[3−5].However,research applying these techniques in this field remains limited.
出处 《Frontiers of Computer Science》 2025年第12期195-197,共3页 计算机科学前沿(英文版)
基金 supported by the 2022 Theoretical Research Project on Talent Work of the China Association for Science and Technology(CAST)titled“Research on Talent Discovery and Utilization Mechanisms for Key Core Technical Personnel:A Case Study in Aerospace,Integrated Circuits,and Related Fields”(2022070607CG0905012022).
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