5 citations · 14 across the 7 of their papers we have counts for
4 papers · 1 filter
Fair Coresets and Streaming Algorithms for Fair k-Means Clustering
Melanie Schmidt, Chris Schwiegelshohn, Christian Sohler
We study fair clustering problems as proposed by Chierichetti et al. (NIPS 2017). Here, points have a sensitive attribute and all clusters in the solution are required to be balanc…
On the cost of essentially fair clusterings
Ioana O. Bercea, Martin Groß, Samir Khuller +4
Clustering is a fundamental tool in data mining. It partitions points into groups (clusters) and may be used to make decisions for each point based on its group. However, this proc…
Turning Big data into tiny data: Constant-size coresets for k-means, PCA and projective clustering
Dan Feldman, Melanie Schmidt, Christian Sohler
We develop and analyze a method to reduce the size of a very large set of data points in a high dimensional Euclidean space R d to a small set of weighted points such that the resu…
Privacy preserving clustering with constraints
Clemens Rösner, Melanie Schmidt
The -center problem is a classical combinatorial optimization problem which asks to find centers such that the maximum distance of any input point in a set to its assign…