## 2 Literature Review
### Data Source of Industrial Clusters Research  
The data source is the key to the accurate data-driven industrial clusters identification process. Based on the data access channels, current research on the cluster identification field mainly discusses economic, social, and scientific data. The collection of data is typically sourced from government-published yearbooks. Such data are authoritative and offer comprehensive coverage of the performance of specific industries or regions, albeit at a macro-level, which limits its ability to facilitate the detailed analysis of individual industries. Moreover, the dataset frequently pertains to a restricted number of industries, without including the cluster status of most industries.  
Social data mainly include social data [34], logistics data [35] and Internet of Things data [36]. Similar to economic data, social data are also often collected from governmental reports from the internet enterprise sector from a general level. Hence, some scholars also use questionnaire survey data to compensate for the disadvantages. Nonetheless, the data collection cost is considerable, but the data size may be comparatively insignificant, thereby rendering it improbable to comprehensively reflect the conditions of the entire industry. In contrast, scientific data mainly include patent data [37] and science data [38]. Current research on scientific data tend to rely on statistical analyses of the quantity of such data within a region or industry as the basis for assessing the innovative or creative potential of the cluster. As patent data are a crucial instrument for protecting and recoding the technological development of various industries, scientific data offer a more precise depiction of the level of technological development, particularly in high-tech industries, when compared to economic or social data [39]. However, current research places greater emphasis on the abundance of literary data, rather than the content of the literature itself. Therefore, a more detailed examination of the subjects encompassed within technology is conducted to achieve accurate identification.  
As is well known, unlike other industries, the high-tech industry relies on patent protection [40]. Therefore, the development of IIC is often accompanied by the emergence of a large number of patents [41]. In this context, this article uses patent big data to conduct research on the identification of IIC.  
In response to the significant challenges in less-documented technological areas or countries, we are concerned that the World Intellectual Property Organization (WIPO) hasso far had a total of 193 member countries, all of which have established patent systems, including major innovative countries, which are most likely to have IIC. As for countries in which a patent system is not well constructed, they are reasonably likely to have a lack of IIC and are thus out of the scope of our study. In addition, the patent literature covers almost all technical areas of human production activities. For example, the technologies covered by patents have a unified international patent classification, which are divided into 8 divisions, 20 sub-divisions, 118 categories, 620 sub-categories, 6871 main groups and 57,320 sub-groups, showing us a very wide range of technical areas.