SCG '94 Proceedings of the tenth annual symposium on Computational geometry
Approximation schemes for clustering problems
Proceedings of the thirty-fifth annual ACM symposium on Theory of computing
Polynomial time approximation schemes for Euclidean TSP and other geometric problems
FOCS '96 Proceedings of the 37th Annual Symposium on Foundations of Computer Science
Clustering Large Graphs via the Singular Value Decomposition
Machine Learning
A local search approximation algorithm for k-means clustering
Computational Geometry: Theory and Applications - Special issue on the 18th annual symposium on computational geometrySoCG2002
A Simple Linear Time (1+ ") -Approximation Algorithm for k-Means Clustering in Any Dimensions
FOCS '04 Proceedings of the 45th Annual IEEE Symposium on Foundations of Computer Science
How fast is the k-means method?
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
How slow is the k-means method?
Proceedings of the twenty-second annual symposium on Computational geometry
Worst-case and Smoothed Analysis of the ICP Algorithm, with an Application to the k-means Method
FOCS '06 Proceedings of the 47th Annual IEEE Symposium on Foundations of Computer Science
The Effectiveness of Lloyd-Type Methods for the k-Means Problem
FOCS '06 Proceedings of the 47th Annual IEEE Symposium on Foundations of Computer Science
k-means++: the advantages of careful seeding
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
On clustering to minimize the sum of radii
Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms
SFCS '80 Proceedings of the 21st Annual Symposium on Foundations of Computer Science
NP-hardness of Euclidean sum-of-squares clustering
Machine Learning
Universality considerations in VLSI circuits
IEEE Transactions on Computers
The directed planar reachability problem
FSTTCS '05 Proceedings of the 25th international conference on Foundations of Software Technology and Theoretical Computer Science
Least squares quantization in PCM
IEEE Transactions on Information Theory
Improving the performance of k-means for color quantization
Image and Vision Computing
A comparative study of efficient initialization methods for the k-means clustering algorithm
Expert Systems with Applications: An International Journal
Hi-index | 5.23 |
In the k-means problem, we are given a finite set S of points in @?^m, 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].