An Extension of the Kullback–Leibler Divergence for the Application of Machine Learning Classification Algorithms to Hierarchical Cluster Analysis

Key Points

• Performance improves with the extended kullback-leibler divergence metric, enhancing classification accuracy.
• The new approach outperforms traditional clustering methods in terms of precision and recall metrics.
• The framework integrates machine learning techniques with hierarchical cluster analysis.
• The findings highlight potential improvements in data grouping for various machine learning applications.