Add to ORKGWe show that k-means clustering is an NP-hard optimization problem, even if k is fixed to 2.
This paper studies the convergence properties of the well known K-Means clustering algorithm. The K-...
Abstract—We consider k-median clustering in finite metric spaces and k-means clustering in Euclidean...
Approximation algorithms for clustering points in metric spaces is a flourishing area of re-search, ...
We show that k-means clustering is an NP-hard optimization problem, even for instances in the plane....
K-Means is one of the most popular clustering algorithms, and it is easy to implement It seeks to m...
We study the problem of finding an optimum clustering, a problem known to be NP-hard. Existing liter...
When clustering a dataset, the right number k of clusters to use is often not obvious, and choosing...
Presented on September 10, 2018 at 11:00 a.m. in the Klaus Advanced Computing Center, Room 1116E.Ana...
In k-Clustering we are given a multiset of n vectors X subset Z^d and a nonnegative number D, and we...
Probably the most famous clustering formulation is k-means. This is the focus today. Note: k-means i...
We discuss a variety of clustering problems arising in combinatorial applications and in classifying...
K-means is one of the simplest unsupervised learning algorithms that solve the well known clustering...
Clustering is often formulated as a discrete optimization problem. The objective is to find, among a...
Working with huge amount of data and learning from it by extracting useful information is one of the...
Abstract—This paper introduces an optimized version of the standard K-Means algorithm. The optimizat...
This paper studies the convergence properties of the well known K-Means clustering algorithm. The K-...
Abstract—We consider k-median clustering in finite metric spaces and k-means clustering in Euclidean...
Approximation algorithms for clustering points in metric spaces is a flourishing area of re-search, ...
We show that k-means clustering is an NP-hard optimization problem, even for instances in the plane....
K-Means is one of the most popular clustering algorithms, and it is easy to implement It seeks to m...
We study the problem of finding an optimum clustering, a problem known to be NP-hard. Existing liter...
When clustering a dataset, the right number k of clusters to use is often not obvious, and choosing...
Presented on September 10, 2018 at 11:00 a.m. in the Klaus Advanced Computing Center, Room 1116E.Ana...
In k-Clustering we are given a multiset of n vectors X subset Z^d and a nonnegative number D, and we...
Probably the most famous clustering formulation is k-means. This is the focus today. Note: k-means i...
We discuss a variety of clustering problems arising in combinatorial applications and in classifying...
K-means is one of the simplest unsupervised learning algorithms that solve the well known clustering...
Clustering is often formulated as a discrete optimization problem. The objective is to find, among a...
Working with huge amount of data and learning from it by extracting useful information is one of the...
Abstract—This paper introduces an optimized version of the standard K-Means algorithm. The optimizat...
This paper studies the convergence properties of the well known K-Means clustering algorithm. The K-...
Abstract—We consider k-median clustering in finite metric spaces and k-means clustering in Euclidean...
Approximation algorithms for clustering points in metric spaces is a flourishing area of re-search, ...