Lin, Wenguang, Wang, Ting, Chen, Zhizhen ORCID: https://orcid.org/0000-0001-6656-5854 and Xiao, Renbin (2024) Data-driven identification of industrial clusters: a patent analysis approach. IEEE Transactions on Engineering Management. ISSN 0018-9391 (Print), 1558-0040 (Online) (doi:10.1109/TEM.2024.3493627)

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Abstract

Accurate Identification of Industrial Clusters (IIC) serves as a reference for regional economic policymaking and enterprise development decision-making. Although data-driven methods have been extensively used in previous studies to support objective and effective work, both the data sources and research algorithms have significant shortcomings for IIC. To address these challenges, this paper proposes a novel research framework that integrates patent mining and machine learning. Patents, with their quantifiable knowledge attributes and accessibility from public databases, are particularly suited for macro-level analysis of innovation activities, providing robust support for identifying and analyzing clusters on a national scale, especially knowledge- intensive ones. This article introduces an improved density-based parameter adaptive algorithm designed to effectively carry out IIC based on the geographical location of patent applicants. Based on spatial cluster types defined by Markusen (1996), target cluster

Item Type:	Article
Uncontrolled Keywords:	patents, clustering algorithms, technological innovation, industries, engineering management, economics, production, adaptation models, heuristic algorithms, accuracy, flexible electronics industry, identification, industrial clusters, patent analysis, spatial distribution
Subjects:	H Social Sciences > H Social Sciences (General) H Social Sciences > HD Industries. Land use. Labor > HD28 Management. Industrial Management Q Science > QA Mathematics
Faculty / School / Research Centre / Research Group:	Greenwich Business School Greenwich Business School > School of Accounting, Finance and Economics
Last Modified:	15 Nov 2024 12:06
URI:	http://gala.gre.ac.uk/id/eprint/48623

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