加速球形k均值
Accelerating Spherical k-Means
摘要：
球形k-means是一种广泛用于稀疏和高维数据（如文档向量）的聚类算法。虽然原始的k-means算法已经引入了一些改进和加速，但并不是所有的都可以轻松地转化为球形变体：许多加速技术，如Elkan和Hamerly的算法，依赖于欧几里得距离的三角不等式。然而，球形k-means使用余弦相似性而不是距离来提高计算效率。在本文中，我们将埃尔坎和Hamerly加速度的球面k-means算法直接与余弦函数，而不是欧几里得距离，以获得实质性的加速，并评估这些球面加速度的真实数据。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
