AINeutralarXiv – CS AI · Mar 37/109
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Universal NP-Hardness of Clustering under General Utilities
Researchers prove that clustering problems in machine learning are universally NP-hard, providing theoretical explanation for why clustering algorithms often produce unstable results. The study demonstrates that major clustering methods like k-means and spectral clustering inherit fundamental computational intractability, explaining common failure modes like local optima.