We study clustering problems such as k-Median, k-Means, and Facility Location in graphs of low highway dimension, which is a graph parameter modeling transportation networks. It was previously shown that approximation schemes for these problems exist, which either run in quasi-polynomial time (assuming constant highway dimension) [Feldmann et al. SICOMP 2018] or run in FPT time (parameterized by the number of clusters k, the highway dimension, and the approximation factor) [Becker et al. ESA 2018, Braverman et al. 2020]. In this paper we show that a polynomial-time approximation scheme (PTAS) exists (assuming constant highway dimension). We also show that the considered problems are NP-hard on graphs of highway dimension 1
We consider generalizations of the $k$-Center problem in graphs of low doubling and highway dimensio...
Abstract—We consider k-median clustering in finite metric spaces and k-means clustering in Euclidean...
We explore the relationship between VC-dimension and graph algorithm design. In particular, we show ...
We study clustering problems such as k-Median, k-Means, and Facility Location in graphs of low highw...
We study clustering problems such as k-Median, k-Means, and Facility Location in graphs of low highw...
The concept of bounded highway dimension was developed to capture observed properties of road networ...
We study the Travelling Salesperson (TSP) and the Steiner Tree problem (STP) in graphs of low highwa...
Clustering problems often arise in fields like data mining and machine learning. Clustering usually ...
We consider the Min-Sum k-Clustering (k-MSC) problem. Given a set of points in a metric which is rep...
In this paper we study the hardness of the k-Center problem on inputs that model transportation netw...
Given a set of n points and their pairwise distances, the goal of clustering is to partition the po...
The completeness of road network data is significant in the quality of various routing services and ...
The two most popular unsupervised learning problems are k-Clustering and Low-Rank Approximation. Con...
Recent developments in local search analysis have yielded the first polynomial-time approximation sc...
We study the Travelling Salesperson (TSP) and the Steiner Tree problem (STP) in graphs of low highwa...
We consider generalizations of the $k$-Center problem in graphs of low doubling and highway dimensio...
Abstract—We consider k-median clustering in finite metric spaces and k-means clustering in Euclidean...
We explore the relationship between VC-dimension and graph algorithm design. In particular, we show ...
We study clustering problems such as k-Median, k-Means, and Facility Location in graphs of low highw...
We study clustering problems such as k-Median, k-Means, and Facility Location in graphs of low highw...
The concept of bounded highway dimension was developed to capture observed properties of road networ...
We study the Travelling Salesperson (TSP) and the Steiner Tree problem (STP) in graphs of low highwa...
Clustering problems often arise in fields like data mining and machine learning. Clustering usually ...
We consider the Min-Sum k-Clustering (k-MSC) problem. Given a set of points in a metric which is rep...
In this paper we study the hardness of the k-Center problem on inputs that model transportation netw...
Given a set of n points and their pairwise distances, the goal of clustering is to partition the po...
The completeness of road network data is significant in the quality of various routing services and ...
The two most popular unsupervised learning problems are k-Clustering and Low-Rank Approximation. Con...
Recent developments in local search analysis have yielded the first polynomial-time approximation sc...
We study the Travelling Salesperson (TSP) and the Steiner Tree problem (STP) in graphs of low highwa...
We consider generalizations of the $k$-Center problem in graphs of low doubling and highway dimensio...
Abstract—We consider k-median clustering in finite metric spaces and k-means clustering in Euclidean...
We explore the relationship between VC-dimension and graph algorithm design. In particular, we show ...