Anna Arutyunova

algo-hhu/fair-k-center-via-outliers

Anna Arutyunova, Annika Hennes, Daniel R. Schmidt

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Exact ratio preservation via outliers for fair $k$-center clustering

Anna Arutyunova, Irina Fast, Annika Hennes, Carsten Krollmann et autres

We study the $k$-center clustering problem under demographic fairness constraints, where the point set is partitioned into groups, and the aim is to compute clusters that exhibit a given group proportion. Previous work in this direction assumes that the entire point set …

Exact Ratio Preservation via Outliers for Fair k-Center Clustering

Anna Arutyunova, Irina Fast, Annika Hennes, Carsten Krollmann et autres

We study the k-center clustering problem under demographic fairness constraints, where the point set is partitioned into groups, and the aim is to compute clusters that exhibit a given group proportion. Previous work in this direction assumes that the entire point set …

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The Price of Hierarchical Clustering

Anna Arutyunova, Heiko Röglin

Abstract Hierarchical Clustering is a popular tool for understanding the hereditary properties of a data set. Such a clustering is actually a sequence of clusterings that starts with the trivial clustering in which every data point forms its own cluster and then …

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Approximately Pareto-optimal Solutions for Bi-Objective k-Clustering

Anna Arutyunova, Jan Eube, Heiko Röglin, Melanie Schmidt et autres

Upper and lower bounds for complete linkage in general metric spaces

Anna Arutyunova, Anna Großwendt, Heiko Röglin, Melanie Schmidt et autres

Abstract In a hierarchical clustering problem the task is to compute a series of mutually compatible clusterings of a finite metric space $$(P,{{\,\textrm{dist}\,}})$$ ( P , dist ) . Starting with the clustering where every point forms its own cluster, one iteratively …

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The Price of Hierarchical Clustering

Anna Arutyunova, Heiko Röglin

Hierarchical Clustering is a popular tool for understanding the hereditary properties of a data set. Such a clustering is actually a sequence of clusterings that starts with the trivial clustering in which every data point forms its own cluster and then successively …

de (code pays fourni par la source)

Achieving Anonymity via Weak Lower Bound Constraints for k-Median and k-Means

Anna Arutyunova, Melanie Schmidt

We study $k$-clustering problems with lower bounds, including $k$-median and $k$-means clustering with lower bounds. In addition to the point set $P$ and the number of centers $k$, a $k$-clustering problem with (uniform) lower bounds gets a number $B$. The solution space …

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