Work

Application of K-means Algorithm for Classification of Beninese Municipalities According to Their Digital Development Profile

Year: 2025

Type: article

Abstract: Background: Optimizing digital territorial development policies requires a thorough understanding of municipal digital profiles and their heterogeneity. This study explores the application of the K-Means clustering algorithm to categorize the 77 Beninese municipalities according to their digital development profile using comprehensive data from the foundational Decision Support System (DSS)described in our companion study. Objective: To develop an innovative methodological approach for analyzing territorial digital disparities and establish municipal typologies to optimize digital territorial planning policies. Methods: Based on a standardized 45-indicator framework across multiple thematic domains collected through our Decision Support System, this research applies K-Means clustering with optimal cluster determination through silhouette analysis. The dataset comprises 20,790 data points providing robust foundation for unsupervised learning analysis.

Source: International Journal of Advanced Research

DOI: 10.21474/ijar01/21587

Authors: Narcisse Arsene Dagba, Aziz Saibou, Theophile Aballo

Institution: Université d'Abomey-Calavi

Language: English

Topic: Data Mining and Machine Learning Applications

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Industry, innovation and infrastructure, Reduced inequalities

Open Access status: hybrid

OpenAlex ID: W4414067732