Authors
David Arthur, Sergei Vassilvitskii
Publication date
2006/6
Journal
Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Pages
1027-1035
Description
The k-means method is a widely used clustering technique that seeks to minimize the average squared distance between points in the same cluster. Although it offers no accuracy guarantees, its simplicity and speed are very appealing in practice. By augmenting k-means with a simple, randomized seeding technique, we obtain an algorithm that is -competitive with the optimal clustering. Experiments show our augmentation improves both the speed and the accuracy of k-means, often quite dramatically.
Total citations
Scholar articles
D Arthur, S Vassilvitskii - 2006
S Vassilvitskii, AD K-means+ - Proceedings of the Eighteenth Annual ACM-SIAM …