论文标题
球k均值
Ball k-means
论文作者
论文摘要
本文介绍了一种称为Ball K-Means算法的新型加速精确的K-均值算法，该算法使用球来描述一个群集，重点是减少点 - 触角距离计算。球k均值只能在每个群集中准确找到邻居簇，仅在一个点及其邻居簇的质心之间而不是所有质心之间进行距离计算。此外，每个簇可以分为一个稳定的区域和一个活跃的区域，然后可以将后者进一步分为环形区域。稳定区域中分配的群集在当前迭代中没有更改，而环区域中的点将在当前迭代中的几个邻居簇中调整。同样，拟议的球K均值中没有上限或下限。此外，降低迭代之间的质心式距离计算使大型K聚类有效。快速速度，没有额外的参数和球K均值的简单设计使其成为幼稚K-均值算法的全方位替代。
This paper presents a novel accelerated exact k-means algorithm called the Ball k-means algorithm, which uses a ball to describe a cluster, focusing on reducing the point-centroid distance computation. The Ball k-means can accurately find the neighbor clusters for each cluster resulting distance computations only between a point and its neighbor clusters' centroids instead of all centroids. Moreover, each cluster can be divided into a stable area and an active area, and the later one can be further divided into annulus areas. The assigned cluster of the points in the stable area is not changed in the current iteration while the points in the annulus area will be adjusted within a few neighbor clusters in the current iteration. Also, there are no upper or lower bounds in the proposed Ball k-means. Furthermore, reducing centroid-centroid distance computation between iterations makes it efficient for large k clustering. The fast speed, no extra parameters and simple design of the Ball k-means make it an all-around replacement of the naive k-means algorithm.
