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Fair k-center clustering for data summarization
In data summarization we want to choose $ k $ prototypes in order to summarize a data set.
We study a setting where the data set comprises several demographic groups and we are …
We study a setting where the data set comprises several demographic groups and we are …
Privacy preserving clustering with constraints
The $ k $-center problem is a classical combinatorial optimization problem which asks to
find $ k $ centers such that the maximum distance of any input point in a set $ P $ to its …
find $ k $ centers such that the maximum distance of any input point in a set $ P $ to its …
Parameterized approximation algorithms for sum of radii clustering and variants
Clustering is one of the most fundamental tools in artificial intelligence, machine learning,
and data mining. In this paper, we follow one of the recent mainstream topics of clustering …
and data mining. In this paper, we follow one of the recent mainstream topics of clustering …
Approximation algorithms for socially fair clustering
We present an (e^{O (p)}(log\ell)/(log log\ell))-approximation algorithm for socially fair
clustering with the l_p-objective. In this problem, we are given a set of points in a metric …
clustering with the l_p-objective. In this problem, we are given a set of points in a metric …
Approximation algorithms for fair range clustering
This paper studies the fair range clustering problem in which the data points are from
different demographic groups and the goal is to pick $ k $ centers with the minimum …
different demographic groups and the goal is to pick $ k $ centers with the minimum …
Improved approximation algorithms for individually fair clustering
We consider the $ k $-clustering problem with $\ell_p $-norm cost, which includes $ k $-
median, $ k $-means and $ k $-center, under an individual notion of fairness proposed by …
median, $ k $-means and $ k $-center, under an individual notion of fairness proposed by …
Fair k-centers via maximum matching
The field of algorithms has seen a push for fairness, or the removal of inherent bias, in recent
history. In data summarization, where a much smaller subset of a data set is chosen to …
history. In data summarization, where a much smaller subset of a data set is chosen to …
Fair and fast k-center clustering for data summarization
We consider two key issues faced by many clustering methods when used for data
summarization, namely (a) an unfair representation of" demographic groups” and (b) …
summarization, namely (a) an unfair representation of" demographic groups” and (b) …
[HTML][HTML] On coresets for fair clustering in metric and euclidean spaces and their applications
Fair clustering is a constrained clustering problem where we need to partition a set of
colored points. The fraction of points of each color in every cluster should be more or less …
colored points. The fraction of points of each color in every cluster should be more or less …
A constant approximation for colorful k-center
In this paper, we consider the colorful $ k $-center problem, which is a generalization of the
well-known $ k $-center problem. Here, we are given red and blue points in a metric space …
well-known $ k $-center problem. Here, we are given red and blue points in a metric space …