We study a generalization of k-center clustering, first introduced by Kavand et. al., where instead of one set of centers, we have two types of centers, p red and q blue, and where each red center is at least ? distant from each blue center. The goal is to minimize the covering radius. We provide an approximation algorithm for this problem, and a polynomial-time algorithm for the constrained problem, where all the centers must lie on a line ?
In the first part of this chapter we present existing work in center based clustering methods. In pa...
AbstractIn k-means clustering we are given a set of n data points in d-dimensional space Rd and an i...
The $k$-center problem with triangle inequality is that of placing $k$ center nodes in a weighted un...
We study a generalization of k-center clustering, first introduced by Kavand et. al., where instead ...
We consider a variant of the k-center clustering problem in IRd, where the centers can be divided in...
An instance of colorful k-center consists of points in a metric space that are colored red or blue, ...
In this paper, we consider the colorful k-center problem, which is a generalization of the well-know...
Introduction Clustering is an important problem, with applications in areas such as data mining and...
Motivated by an application from geodesy, we introduce a novel clustering problem which is a $k$-cen...
Clustering is an important problem and has numerous applications. In this paper we consider an impor...
Center-based clustering is a fundamental primitive for data analysis and becomes very challenging fo...
In the Non-Uniform k-Center (NUkC) problem, a generalization of the famous k-center clustering probl...
In this thesis we show that, for several clustering problems, we can extract a small set of points, ...
In this paper we deal with the vertex k-center problem, a problem which is a part of the discrete lo...
The classical center based clustering problems such as k-means/median/center assume that the optimal...
In the first part of this chapter we present existing work in center based clustering methods. In pa...
AbstractIn k-means clustering we are given a set of n data points in d-dimensional space Rd and an i...
The $k$-center problem with triangle inequality is that of placing $k$ center nodes in a weighted un...
We study a generalization of k-center clustering, first introduced by Kavand et. al., where instead ...
We consider a variant of the k-center clustering problem in IRd, where the centers can be divided in...
An instance of colorful k-center consists of points in a metric space that are colored red or blue, ...
In this paper, we consider the colorful k-center problem, which is a generalization of the well-know...
Introduction Clustering is an important problem, with applications in areas such as data mining and...
Motivated by an application from geodesy, we introduce a novel clustering problem which is a $k$-cen...
Clustering is an important problem and has numerous applications. In this paper we consider an impor...
Center-based clustering is a fundamental primitive for data analysis and becomes very challenging fo...
In the Non-Uniform k-Center (NUkC) problem, a generalization of the famous k-center clustering probl...
In this thesis we show that, for several clustering problems, we can extract a small set of points, ...
In this paper we deal with the vertex k-center problem, a problem which is a part of the discrete lo...
The classical center based clustering problems such as k-means/median/center assume that the optimal...
In the first part of this chapter we present existing work in center based clustering methods. In pa...
AbstractIn k-means clustering we are given a set of n data points in d-dimensional space Rd and an i...
The $k$-center problem with triangle inequality is that of placing $k$ center nodes in a weighted un...