1 citations · 2 across the 6 of their papers we have counts for
7 papers
Round-efficient Fully-scalable MPC algorithms for k-Means
Shaofeng H. -C. Jiang, Yaonan Jin, Jianing Lou +1
We study Euclidean -Means under the Massively Parallel Computation (MPC) model, focusing on the \emph{fully-scalable} setting. Our main result is a fully-scalable $O((\log n/\lo…
Fully Dynamic Euclidean k-Means
Sayan Bhattacharya, Martín Costa, Ermiya Farokhnejad +3
We consider the Euclidean -means clustering problem in a dynamic setting, where we have to explicitly maintain a solution (a set of centers) subje…
Local Search for Clustering in Almost-linear Time
Shaofeng H. -C. Jiang, Yaonan Jin, Jianing Lou +1
We propose the first \emph{local search} algorithm for Euclidean clustering that attains an -approximation in almost-linear time. Specifically, for Euclidean -Means, our a…
Coresets for Robust Clustering via Black-box Reductions to Vanilla Case
Shaofeng H. -C. Jiang, Jianing Lou
We devise -coresets for robust -Clustering with outliers through black-box reductions to vanilla case. Given an -coreset construction for vanilla clustering with s…
The Power of Uniform Sampling for -Median
Lingxiao Huang, Shaofeng H. -C. Jiang, Jianing Lou
We study the power of uniform sampling for -Median in various metric spaces. We relate the query complexity for approximating -Median, to a key parameter of the dataset, call…
Near-optimal Coresets for Robust Clustering
Lingxiao Huang, Shaofeng H. -C. Jiang, Jianing Lou +1
We consider robust clustering problems in , specifically -clustering problems (e.g., -Median and -Means with outliers, where the cost for a given center…
Coresets for Kernel Clustering
Shaofeng H. -C. Jiang, Robert Krauthgamer, Jianing Lou +1
We devise coresets for kernel -Means with a general kernel, and use them to obtain new, more efficient, algorithms. Kernel -Means has superior clustering capability compared…