@cscholz

k-means++: the advantages of careful seeding

, and . SODA '07: Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, page 1027--1035. Philadelphia, PA, USA, Society for Industrial and Applied Mathematics, (2007)

Abstract

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 very simple, randomized seeding technique, we obtain an algorithm that is Θ(logk)-competitive with the optimal clustering. Preliminary experiments show that our augmentation improves both the speed and the accuracy of k-means, often quite dramatically.

Description

k-means++

Links and resources

Tags

community

  • @cscholz
  • @cdevries
  • @lama
  • @dblp
  • @sdo
@cscholz's tags highlighted