AbstractIn the k-means problem, we are given a finite set S of points in ℜm, and integer k≥1, and we want to find k points (centers) so as to minimize the sum of the square of the Euclidean distance of each point in S to its nearest center. We show that this well-known problem is NP-hard even for instances in the plane, answering an open question posed by Dasgupta (2007) [7]
The k-means algorithm is a well-known method for parti-tioning n points that lie in the d-dimensiona...
Center-based clustering is a fundamental primitive for data analysis and becomes very challenging fo...
Introduction Clustering is an important problem, with applications in areas such as data mining and...
AbstractIn the k-means problem, we are given a finite set S of points in ℜm, and integer k≥1, and we...
The Euclidean k-means problem is a classical problem that has been extensively studied in the theore...
Clustering problems often arise in fields like data mining and machine learning. Clustering usually ...
We show that k-means clustering is an NP-hard optimization problem, even for instances in the plane....
AbstractIn this paper, we consider the problem of clustering a set of n finite point-sets in d-dimen...
We study the min-size $k$-clustering problem, a geometric clustering problem which generalizes clust...
AbstractIn k-means clustering we are given a set of n data points in d-dimensional space Rd and an i...
summary:It is shown that the problem of finding a minimum $k$-basis, the $n$-center problem, and the...
In the Euclidean k-Means problem we are given a collection of n points D in an Euclidean space and a...
Probably the most famous clustering formulation is k-means. This is the focus today. Note: k-means i...
The k-means method is a widely used technique for clustering points in Euclidean space. While it is...
The classical center based clustering problems such as k-means/median/center assume that the optimal...
The k-means algorithm is a well-known method for parti-tioning n points that lie in the d-dimensiona...
Center-based clustering is a fundamental primitive for data analysis and becomes very challenging fo...
Introduction Clustering is an important problem, with applications in areas such as data mining and...
AbstractIn the k-means problem, we are given a finite set S of points in ℜm, and integer k≥1, and we...
The Euclidean k-means problem is a classical problem that has been extensively studied in the theore...
Clustering problems often arise in fields like data mining and machine learning. Clustering usually ...
We show that k-means clustering is an NP-hard optimization problem, even for instances in the plane....
AbstractIn this paper, we consider the problem of clustering a set of n finite point-sets in d-dimen...
We study the min-size $k$-clustering problem, a geometric clustering problem which generalizes clust...
AbstractIn k-means clustering we are given a set of n data points in d-dimensional space Rd and an i...
summary:It is shown that the problem of finding a minimum $k$-basis, the $n$-center problem, and the...
In the Euclidean k-Means problem we are given a collection of n points D in an Euclidean space and a...
Probably the most famous clustering formulation is k-means. This is the focus today. Note: k-means i...
The k-means method is a widely used technique for clustering points in Euclidean space. While it is...
The classical center based clustering problems such as k-means/median/center assume that the optimal...
The k-means algorithm is a well-known method for parti-tioning n points that lie in the d-dimensiona...
Center-based clustering is a fundamental primitive for data analysis and becomes very challenging fo...
Introduction Clustering is an important problem, with applications in areas such as data mining and...
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