Optimization of Fuzzy C-Means Clustering with Particle Swarm Optimization on Socioeconomic Indicators of ASEAN Countries

Cindy Indriyani (Study Program on Statistics and Data Science, IPB University, Indonesia)
Siti Arbaynah (Study Program on Statistics and Data Science, IPB University, Indonesia)
Ananda Putra Wijaya (Study Program on Statistics and Data Science, IPB University, Indonesia)
Lusi Oktaviani (Study Program on Statistics and Data Science, IPB University, Indonesia)
Fadhilah Yumna (Study Program on Statistics and Data Science, IPB University, Indonesia)
Norashida Othman (Department of Business and Management, Universiti Teknologi MARA, Malaysia)
Sachnaz Desta Oktarina (Study Program on Statistics and Data Science, IPB University, Indonesia)
Rahma Anisa (Study Program on Statistics and Data Science, IPB University, Indonesia)

Indonesian Journal of Statistics and Its Applications
Vol 9 No 2 (2025)

Grouping data based on similarity in characteristics is commonly applied in various exploratory analyses. The Fuzzy C-Means algorithm offers flexibility through the degree of membership of data points in each cluster, but it is vulnerable to poor cluster center initialization, which increases the risk of getting trapped in local optima. To enhance the performance of Fuzzy C-Means, this study integrates the Particle Swarm Optimization method for determining cluster centers. The evaluation is conducted by comparing Fuzzy C-Means and Fuzzy C-Means-Particle Swarm Optimization across several cluster counts using three internal validation metrics, namely the silhouette coefficient, partition coefficient, and Xie-Beni Index. The results show that Fuzzy C-Means-Particle Swarm Optimization consistently yields higher silhouette coefficient and partition coefficient values, along with lower Xie-Beni Index values, compared to standard Fuzzy C-Means. This indicates that the integration of Particle Swarm Optimization can improve clustering quality in terms of cluster compactness and separation. This hybrid approach demonstrates significant potential in complex data clustering scenarios.