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cs.LG2024
Settling Time vs. Accuracy Tradeoffs for Clustering Big Data
Andrew Draganov, David Saulpic, Chris Schwiegelshohn
We study the theoretical and practical runtime limits of k-means and k-median clustering on large datasets. Since effectively all clustering methods are slower than the time it tak…
cs.LG2024★ 1 cited
Data-Efficient Learning via Clustering-Based Sensitivity Sampling: Foundation Models and Beyond
Kyriakos Axiotis, Vincent Cohen-Addad, Monika Henzinger +5
We study the data selection problem, whose aim is to select a small representative subset of data that can be used to efficiently train a machine learning model. We present a new d…