Why Unsupervised Clustering Misleads More Than It Helps: A Methodological Critique
shakoistsLog · x · 2026-08-27
The thread argues the core problem with high-dimensional clustering: dimensions aren't equally utility-weighted — weak clusters on dims 7-9 can be extremely predictive while dominant clusters on dims 1-4 are uninteresting. Unsupervised clustering is "more misleading than helpful"; the fix is regression on orthogonal dimensions with a supervised Y, which surfaces the real debates over Y's correctness and causal interpretation.
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