Abstract

This study employs cluster analysis to segment Micro, Small, and Medium-sized Enterprises (MSMEs) in Suleja, Nigeria into distinct groups based on their financial and operational characteristics. We utilize Fuzzy C-Means Clustering and t-Distributed Stochastic Neighbor Embedding (t-SNE) visualizations to uncover hidden patterns and structures in the data. Our results reveal three distinct clusters, explaining 40.3% of the variance in the data (R² = 0.403). The clusters exhibit reasonable separation (Silhouette coefficient = 0.360) and distinct patterns in their variable means. Cluster 1 comprises high-performing MSMEs with high levels of engagement, participation, and satisfaction. Cluster 2 consists of low-performing MSMEs with low levels of engagement, participation, and satisfaction. Cluster 3 represents moderate-performing MSMEs with balanced levels of engagement, participation, and satisfaction. t-SNE visualizations confirm the clustering structure, revealing three distinct groups with varying densities and separation. The visualizations also highlight the relationships between the clusters, with Cluster 1 and Cluster 3 showing higher similarity than Cluster 2. Model performance metrics indicate a reasonable accuracy (Pearson's γ = 0.440) and a good balance between within-cluster sum of squares and between-cluster sum of squares (Calinski-Harabasz index = 13.087).  The findings provide valuable insights for policymakers and practitioners seeking to support MSMEs in Nigeria. The results suggest that targeted interventions and support programs should focus on enhancing engagement, participation, and satisfaction among MSMEs, particularly for those in the low-performing cluster. The study contributes to the literature on MSMEs and cluster analysis, demonstrating the effectiveness of Fuzzy C-Means Clustering and t-SNE visualizations in uncovering meaningful patterns in MSMEs data.