Abstract:We provide a more efficient algorithm for computing the Rand Index when the data clusters come from a change-point detection problem. Given the number of data points $N$ and two change-point sets of size $r$ and $s$, the algorithm runs on $O(r+s)$ time complexity and $O(1)$ memory complexity. The Rand Index computation for the general clustering problem, in contrast, requires the $N$ cluster memberships and has a $O(N)$ complexity in both time and memory.
From: Lucas Prates [view email]
[v1]
Tue, 7 Dec 2021 14:52:04 UTC (33 KB)
[v2]
Thu, 19 Jun 2025 12:11:34 UTC (73 KB)
[v3]
Sat, 22 Aug 2026 03:45:38 UTC (69 KB)
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