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Accelerating Spherical k-Means by Schubert, Erich; Lang, Andreas; Feher, Gloria is a scholarly article available to read on EtoBox.

What is Accelerating Spherical k-Means about?

Spherical k-means is a widely used clustering algorithm for sparse and high-dimensional data such as document vectors. While several improvements and accelerations have been introduced for the original k-means algorithm, not all easily translate to the spherical variant: Many acceleration techniques, such as the algorithms of Elkan and Hamerly, rely on the triangle inequality of Euclidean distances. However, spherical k-means uses Cosine similarities instead of distances for computational efficiency. In this paper, we incorporate the Elkan and Hamerly accelerations to the spherical k-means algorithm working directly with the Cosines instead of Euclidean distances to obtain a substantial speedup and evaluate these spherical accelerations on real data.

Author

Schubert, Erich; Lang, Andreas; Feher, Gloria

Published

2021

Language

EN