Regional economic development level assessment based on K-means clustering algorithm

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...

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Bibliographic Details
Published in:Procedia computer science Vol. 262; pp. 1137 - 1143
Main Authors: Pang, Yifan, Nie, Dan
Format: Journal Article
Language:English
Published: Elsevier B.V 2025
Subjects:
Development level assessment
K-means clustering algorithm
Regional economy
Regional economy
K-means clustering algorithm
Development level assessment
ISSN:1877-0509, 1877-0509
Online Access:Get full text
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Summary: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.
ISSN:1877-0509
1877-0509
DOI:10.1016/j.procs.2025.05.152