Comparative Analysis of Improved K-Means Clustering for Human Freedom Index

The Human Freedom Index (HFI) assesses the extent of human freedom in different nations by considering both personal and economic freedoms. It incorporates measures such as the rule of law, freedom of expression, and freedom to trade, giving a holistic picture of the freedoms people have in different countries. In order to improve the HFI analysis, this research examines three advanced clustering strategies. The first technique optimises clustering performance and minimises iterations by combining the Hamely algorithm with Mini Batch k-means (MBK-means). The second method uses chromosomes to construct cluster centroids, therefore leveraging an Improved Genetic Algorithm (GA) based K-means (IGBKM) to address concerns with initial cluster centre sensitivity and local optimisation. The third and most promising method combines Ant Colony Optimisation (ACO) and Improved K-means (ACO-IKM) to solve the shortcomings of K-means clustering by selecting cluster centroids optimally and maximising ability based on the disparity statistics. This comparative research study shows that in terms of clustering robustness and accuracy, the ACO-IKM method performs much better than the other approaches. The results of this study show that the ACO-IKM approach is a better way to analyse the HFI dataset, providing dependable and effective clustering performance using Silhouette score. This assists in identifying trends in global freedom and pinpoints areas that require improvement in order to advance human dignity and autonomy.