K-Means++和K-Means $\|$的精确加速
Exact Acceleration of K-Means++ and K-Means$\|$
摘要：
K-Means++及其分布式变体K-Means $\|$已成为选择K-means初始种子的实际工具。虽然已经开发了替代方案，但K-means++和$\|$方法的有效性、易于实现性和理论基础使得它们难以从整体角度“最佳”。通过考虑种子选择中执行修剪的有限机会，我们开发了专门的三角不等式修剪策略和动态优先级队列，以显示K-Means++和K-Means $\|$的第一加速，该加速在运行时更快，同时在算法上是等价的。对于这两种算法，我们能够减少距离计算超过$500\times$。对于K-means++，这将导致运行时的$\times$加速高达17倍，而对于K-means $\|$，这将导致$551\times$加速。我们实现了这一点，简单的，但仔细选择，修改已知的技术，这使得它很容易将我们的方法集成到现有的实现这些算法。
英文摘要：
K-Means++ and its distributed variant K-Means$\|$ have become de facto tools for selecting the initial seeds of K-means. While alternatives have been developed, the effectiveness, ease of implementation, and theoretical grounding of the K-means++ and $\|$ methods have made them difficult to "best" from a holistic perspective. By considering the limited opportunities within seed selection to perform pruning, we develop specialized triangle inequality pruning strategies and a dynamic priority queue to show the first acceleration of K-Means++ and K-Means$\|$ that is faster in run-time while being algorithmicly equivalent. For both algorithms we are able to reduce distance computations by over $500\times$. For K-means++ this results in up to a 17$\times$ speedup in run-time and a $551\times$ speedup for K-means$\|$. We achieve this with simple, but carefully chosen, modifications to known techniques which makes it easy to integrate our approach into existing implementations of these algorithms.
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。
