APPLICATION OF FUZZY C-MEANS CLUSTERING ALGORITHM FOR SPATIAL STRATIFIED HETEROGENEITY OF INNOVATION CAPABILITY OF NATIONAL HIGH-TECH ZONES IN CHINA
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Abstract
The objectives of the study were to construct a comprehensive and scientific evaluation index system to assess the innovation capabilities of 169 national high-tech zones in China, analyze the spatial stratified heterogeneity of innovation capabilities across 34 provinces, and visualize and analyze innovation capabilities' spatial distribution and disparities. The research adopted a multi-method approach, including the entropy weight method, catastrophe progression method, weighted average method, and fuzzy c-means clustering algorithm, supplemented by data visualization using Python-based tools. The results showed that the evaluation index system consisted of four levels and 28 indicators, effectively quantifying the innovation performance. Catastrophe progression results indicated that Beijing Zhongguancun (0.9725), Shanghai (0.9531), and Shenzhen (0.9472) rank highest, while Rongchang (0.7810), Huainan (0.7947), and Qianjiang (0.7968) rank lowest. The fuzzy c-means clustering analysis classified provinces into six distinct categories of innovation capability, revealing a pronounced "strong East, weak West" spatial pattern. The findings offered spatially grounded policy insights that was to bridge regional innovation gaps, China should enhance R&D investment, optimize resource allocation, promote innovation output conversion, and strengthen inter-regional cooperation, particularly in less developed regions. These measures supported the goal of achieving balanced regional innovation and sustainable national development.
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