Quantifies how partition stability, minority class recovery, and topological interpretability trade off across K-means, SOMs, consensus K-means, and four modern baselines on 10,095 TCGA cancer RNA samples (13,634 genes, 19 cancer types). At matched prototype budgets, K-means outperforms SOMs on both stability and minority recovery, while SOMs uniquely provide 2D topological visualization. No single method optimizes all three properties.
Applies machine learning modeling to identify cancer subtypes from gene expression profiles, advancing computational approaches to precision oncology.
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