5 citations · 14 across the 6 of their papers we have counts for
10 papers · 1 filter
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…
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…
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…
Analysis of Ward's Method
Anna Großwendt, Heiko Röglin, Melanie Schmidt
We study Ward's method for the hierarchical -means problem. This popular greedy heuristic is based on the \emph{complete linkage} paradigm: Starting with all data points as sing…
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…