Privacy preserving clustering with constraints
C Rösner, M Schmidt - arXiv preprint arXiv:1802.02497, 2018 - arxiv.org
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 …
[HTML][HTML] How to find a good explanation for clustering?
Abstract k-means and k-median clustering are powerful unsupervised machine learning
techniques. However, due to complicated dependencies on all the features, it is challenging …
techniques. However, due to complicated dependencies on all the features, it is challenging …
A pairwise fair and community-preserving approach to k-center clustering
Clustering is a foundational problem in machine learning with numerous applications. As
machine learning increases in ubiquity as a backend for automated systems, concerns …
machine learning increases in ubiquity as a backend for automated systems, concerns …
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 …
A Technique for Obtaining True Approximations for k-Center with Covering Constraints
There has been a recent surge of interest in incorporating fairness aspects into classical
clustering problems. Two recently introduced variants of the k-Center problem in this spirit …
clustering problems. Two recently introduced variants of the k-Center problem in this spirit …
End-to-end pareto set prediction with graph neural networks for multi-objective facility location
The facility location problems (FLPs) are a typical class of NP-hard combinatorial
optimization problems, which are widely seen in the supply chain and logistics. Many …
optimization problems, which are widely seen in the supply chain and logistics. Many …
Approximation schemes for clustering with outliers
Clustering problems are well studied in a variety of fields, such as data science, operations
research, and computer science. Such problems include variants of center location …
research, and computer science. Such problems include variants of center location …
Fair Colorful k-Center Clustering
An instance of colorful k-center consists of points in a metric space that are colored red or
blue, along with an integer k and a coverage requirement for each color. The goal is to find …
blue, along with an integer k and a coverage requirement for each color. The goal is to find …
Fair colorful k-center clustering
An instance of colorful k-center consists of points in a metric space that are colored red or
blue, along with an integer k and a coverage requirement for each color. The goal is to find …
blue, along with an integer k and a coverage requirement for each color. The goal is to find …
Faster Query Times for Fully Dynamic -Center Clustering with Outliers
Given a point set $ P\subseteq M $ from a metric space $(M, d) $ and numbers $ k, z\in N $,
the* metric $ k $-center problem with $ z $ outliers* is to find a set $ C^\ast\subseteq P $ of …
the* metric $ k $-center problem with $ z $ outliers* is to find a set $ C^\ast\subseteq P $ of …