Chris Schwiegelshohn

activity

20152026

most citedA New Coreset Framework for Clustering

32 citations · 63 across the 14 of their papers we have counts for

collaborators

Showing 2024Show all

3 papers · 1 filter

cs.DS2024

On Approximability of $ℓ_{2}^{2}$ Min-Sum Clustering

Karthik C. S., Euiwoong Lee, Yuval Rabani +2

The $ℓ_{2}^{2}$ min-sum $k$ -clustering problem is to partition an input set into clusters $C_{1}, \dots, C_{k}$ to minimize $\sum_{i = 1}^{k} \sum_{p, q \in C_{i}} ∥ p - q ∥_{2}^{2}$ . Although $\ell_2^2…

cs.DS2024

Sensitivity Sampling for $k$ -Means: Worst Case and Stability Optimal Coreset Bounds

Nikhil Bansal, Vincent Cohen-Addad, Milind Prabhu +2

Coresets are arguably the most popular compression paradigm for center-based clustering objectives such as $k$ -means. Given a point set $P$ , a coreset $Ω$ is a small, weighted summ…

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…

On Approximability of ℓ22​ Min-Sum Clustering

Sensitivity Sampling for k-Means: Worst Case and Stability Optimal Coreset Bounds

Settling Time vs. Accuracy Tradeoffs for Clustering Big Data

On Approximability of $ℓ_{2}^{2}$ Min-Sum Clustering

Sensitivity Sampling for $k$ -Means: Worst Case and Stability Optimal Coreset Bounds