In this paper we provide a more efficient algorithm to compute the Rand Index when the data cluster comes from change-point detection problems. Given $N$ data points and two clusters of size $r$ and $s$, the algorithm runs on $O(r+s)$ time complexity and $O(1)$ memory complexity. The traditional algorithm, in contrast, runs on $O(rs+N)$ time complexity and $O(rs)$ memory complexity.

翻译：在本文中,当数据组来自变化点探测问题时,我们提供了一种更高效的算法来计算兰特指数。考虑到美元的数据点和两个大小的组,美元和美元,算法以美元(r+s)的时间复杂度和1美元记忆复杂度计算。 相比之下,传统的算法则以美元(r+N)的时间复杂度和美元(rs)的记忆复杂度计算。