## 5 Discussion
### Impact on Industry Cluster Research  
The proposed method leverages patent data and their associated benefits to facilitate the accurate identification of IIC in terms of technological and spatial dimensions. It operates in an unsupervised manner and effectively determines the boundaries of regional clusters while also providing a systematic analysis of their technological status and patterns. This research serves as a foundation for the subsequent measurement of regional industrial clusters and the formulation of relevant policies by scholars and government authorities.  
Government agencies and researchers can use this method to precisely understand the distribution of industrial clusters in various technology industries. Since a patent is the important vehicle for existing enterprises seeking to protect their technology and an indispensable tool for technology enterprises in various industries seeking to achieve market competitiveness, it can be applied to most technology industries with the help of patent big data. Analyzing the development of the industry with the help of patents is the mainstream method of existing technology management. At the same time, with the help of patent applicants and address texts, the identification of clusters can be narrowed down from a national level to a certain region, such as the one shown in Figure 8, and can be focused on the city level or county level, or even smaller, through the map API that can accurately obtain the longitude and latitude positioning of enterprises. This is unmatched by other available data. It should be added that some scholars have also used patent classification numbers to classify technologies [82]. However, for most industries, the technological content that patent classification numbers can provide is still too broad, while lacking annotation for many technologies, such as manufacturing processes and materials in the flexible electronics industry, which do not have corresponding classification numbers and obviously cannot meet the needs of industrial technology mining [83].  
From an algorithmic standpoint, the present paper introduces the MDBSCAN algorithm, which effectively clusters spatial groups by incorporating additional indicators. The implemented code demonstrates the algorithm's capability to accommodate multiple indicators for clustering purposes. While the computation time of MDBSCAN may be higher than that of alternative algorithms, it not only preserves the original algorithm's ability to identify intricate contours but also addresses the limitations of indicator scarcity. Furthermore, the algorithm exhibits superior accuracy compared to other approaches, thus offering substantial advantages that outweigh any drawbacks. Consequently, this algorithm represents a valuable contribution, furnishing novel analytical tools and conceptual insights for cluster analysis research. Not only that, this paper also chooses Chinese flexible electronics patents as the research object. On the one hand, it is because Baidu API can automatically obtain the latitude and longitude based on the detailed address information provided by Chinese patents, and on the other hand, flexible electronics have been highly valued by Chinese governments at all levels in recent years. More than this, the Baidu news index ([[https://index.baidu.com/v2/main/index.html#/crowd/oel?words=oel](https://index.baidu.com/v2/main/index.html#/crowd/oel?words=oel)]([https://index.baidu.com/v2/main/index.html#/crowd/oel?words=oel](https://index.baidu.com/v2/main/index.html#/crowd/oel?words=oel)), accessed on 31 December 2022) shows that Beijing, Yangtze River Delta, Pearl River Delta, and Sichuan (cluster number c4) are currently hot regions of FEI in China, as depicted in Figure 11, in which the blue shading represents the exploratory result. Therefore, this also further proves the effectiveness of the method proposed in this article. Of course, the method is also applicable to the analysis of patents and their industrial clusters in other countries, if the API platform supports it.