Regional economic development level assessment based on K-means clustering algorithm
Year: 2025
Type: conference-paper
Abstract: In order to quantitatively analyze regional economy through scientific method, the differences of economic development in different regions are revealed. This paper uses K-means clustering algorithm to divide regional economic data into several groups, and each group represents a region with similar economic development level. On this basis, the index system of regional economic development level including a number of economic indicators is constructed, and the accuracy and consistency of data are ensured through data collection and pre-processing. The experimental results show that the K-means clustering algorithm can effectively divide the regional economy into several groups with different development levels, and the regions within each group have significant similarities in economic development, while there are obvious differences between different groups.
Source: Procedia Computer Science
Authors: Yifan Pang, Dan Nie
Institution: Dalian University of Finance and Economics
Language: English
Subfield: Economics and Econometrics
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4411175804