First sampling algorithms with O(ε) additive error guarantees for local and global silhouette estimation in metric k-clustering, using O(nk ε^{-2} ln(nk/δ)) distances, plus constant-round distributed MapReduce/MPC versions.
A FAST k-MEANS IMPLEMENTATION USING CORESETS , Url =
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TabKDE generates synthetic tabular data using copula transformations followed by kernel density estimation, matching prior accuracy with negligible training time and reduced storage via coresets.
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Scalable and Distributed Silhouette Approximation
First sampling algorithms with O(ε) additive error guarantees for local and global silhouette estimation in metric k-clustering, using O(nk ε^{-2} ln(nk/δ)) distances, plus constant-round distributed MapReduce/MPC versions.