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Clustering-based features in machine learning often require a fixed resolution choice. Recent work shows varying this parameter yields a limited set of structural outcomes, which this method aims to improve. The real impact is on downstream prediction tasks where one-size-fits-all clustering falls short.
“arXiv:2510.19248v2 Announce Type: replace Abstract: Clustering-based features are widely used in machine learning, but most methods must choose a resolution -- a choice that is global, fixed, and ad hoc. Recent work shows that varying the r…”
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arXiv:2510.19248v2
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Aug 5, 2026