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.
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TabKDE: Simple and Scalable Tabular Data Generation with Kernel Density Estimates
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.