[Objective] This study targets to improve the ability to discover research layout of funding agencies based on their funding application documents. [Methods] The K-means++ clustering method is proposed to analyse the funding direction and main focus based on multiple sources of funding application documents. [Results] After validation and a case study based on the funding application abstracts from NSF and FP, it is discovered that single-word feature is more accurate than multi-word feature in the K-means++ clustering. If only keep the essential contents of application abstrcts as analysis documents, the accuracy of the K-means++ clustering is significantly improved. [Limitations] Data cleaning of the funding application documents is not fully automated. Adjustment of clustering parameters need to be manually controlled. [Conclusions] The K-means++ clustering of funding application documents is a practicable method by validation and case study. Research layout differences in agencies' funding trends are discovered and could be helpful for scientific management and policy decision.
陈挺, 韩涛, 李泽霞, 李国鹏, 王小梅. 科研项目布局差异对比方法研究——以NSF和EUFP项目为例[J]. 现代图书情报技术, 2015, 31(7-8): 89-96.
Chen Ting, Han Tao, Li Zexia, Li Guopeng, Wang Xiaomei. Research on Comparison Method of Scientific Funding Layout——Take NSF and EU FP Grants for Instance. New Technology of Library and Information Service, 2015, 31(7-8): 89-96.
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