Unsupervised Learning: Economic Freedom in OECD Countries with Cluster Analysis
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https://doi.org/10.5281/zenodo.10429529Keywords:
Unsupervised Learning, Clustering Analysis, Genetic Algorithm, Simulated Annealing, Levels of Economic FreedomAbstract
This study was carried out using cluster analysis, one of the unguided learning methods, to evaluate the economic freedom levels of OECD countries in 2023. With the optimal k value determined by using genetic algorithms and annealing simulation optimization algorithms, countries were examined with two different k-means clustering analyses. According to the results of the analysis, the annealing simulation method performed better than the genetic algorithm method. The high silhouette score indicates that the clusters formed by the simulated annealing method are more homogeneous and well separated from each other, while the low clustering error indicates that these clusters are closer and more distinct from the data points. The results of this study make an important contribution by providing a scientifically based approach to assessing levels of economic freedom and shaping future economic policies.
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