activity
20132026
most citedFair Coresets and Streaming Algorithms for Fair k-Means Clustering

35 citations · 74 across the 13 of their papers we have counts for

collaborators

17 papers

cs.LG2026

Connected Subspace Clustering: Hardness, a Scalable Heuristic, and an Application to Sea Level Geodesy

Johanna Hillebrand, Jan Höckendorff, Jürgen Kusche +5

Constrained optimization extends classical optimization by integrating side information, making it widely applicable across scientific and engineering domains. Consider a setting w…

cs.DS2022

Connected k-Center and k-Diameter Clustering

Lukas Drexler, Jan Eube, Kelin Luo +4

Motivated by an application from geodesy, we introduce a novel clustering problem which is a -center (or k-diameter) problem with a side constraint. For the side constraint, we…

cs.DS2021★ 1 cited

Coresets for constrained k-median and k-means clustering in low dimensional Euclidean space

Melanie Schmidt, Julian Wargalla

We study (Euclidean) -median and -means with constraints in the streaming model. There have been recent efforts to design unified algorithms to solve constrained -means pr…

cs.DS2020★ 4 cited

Achieving anonymity via weak lower bound constraints for k-median and k-means

Anna Arutyunova, Melanie Schmidt

We study -clustering problems with lower bounds, including -median and -means clustering with lower bounds. In addition to the point set and the number of centers ,…

cs.DS2019★ 5 cited

Noisy, Greedy and Not So Greedy k-means++

Anup Bhattacharya, Jan Eube, Heiko Röglin +1

The k-means++ algorithm due to Arthur and Vassilvitskii has become the most popular seeding method for Lloyd's algorithm. It samples the first center uniformly at random from the d…

cs.DS2019★ 6 cited

Fully dynamic hierarchical diameter k-clustering and k-center

Melanie Schmidt, Christian Sohler

We develop dynamic data structures for maintaining a hierarchical k-center clustering when the points come from a discrete space . Our first data structure is for…