H. Soliman

Quantifying the Stability–Recovery–Interpretability Trade-Off Between K-Means and Self-Organizing Maps for High-Dimensional Imbalanced Data

High-dimensional engineering datasets often combine class imbalance, noisy structures, and limited ground truth, making unsupervised analysis difficult to evaluate reliably. This study quantifies how three properties—partition stability, minority class recovery, and topological interpretability—are traded off across cl...