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A more efficient algorithm to compute the Rand Index for change-point problems
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abstract
We provide a more efficient algorithm for computing the Rand Index when the data cluster comes from a change-point detection problem. Given $N$ data points and two clusterings 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.
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Cited by 1 Pith paper
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Fast segmentation of watermarked texts from large language models through an epidemic change-point framework
WISER is a linear-time, provably consistent algorithm that localizes multiple watermarked segments in mixed-source texts by treating pivot statistics as an epidemic change-point sequence.
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