k-means++: the advantages of careful seeding

David Arthur; Sergei Vassilvitskii · OpenAlex · 2007

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David Arthur; Sergei Vassilvitskii

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OpenAlex

Year

2007

Language

English

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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 O(log k)-competitive with the optimal clustering. Experiments show our augmentation improves both the speed and the accuracy of k-means, often quite dramatically. 1

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APA 7

Arthur, D. & Vassilvitskii, S. (2007). k-means++: the advantages of careful seeding. https://doi.org/10.5555/1283383.1283494

MLA

Arthur, David, and Sergei Vassilvitskii. "k-means++: the advantages of careful seeding." 2007. https://doi.org/10.5555/1283383.1283494.

Chicago

Arthur, David and Sergei Vassilvitskii. 2007. "k-means++: the advantages of careful seeding.". https://doi.org/10.5555/1283383.1283494.

Harvard

Arthur, D. and Vassilvitskii, S. 2007, k-means++: the advantages of careful seeding, OpenAlex, available at: https://doi.org/10.5555/1283383.1283494 [Accessed 8 Aug. 2026].

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Title: k-means++: the advantages of careful seeding

Author / contributors: David Arthur; Sergei Vassilvitskii

Publisher: OpenAlex

Publication year: 2007

Language: English

Subjects

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Seeding; Cluster analysis; Computer science; Simplicity; Simple (philosophy); k-means clustering; Algorithm; Cluster (spacecraft); Data mining; Artificial intelligence; Mathematical optimization; Mathematics