# FastAMI -- a Monte Carlo Approach to the Adjustment for Chance in Clustering Comparison Metrics

arXiv: https://arxiv.org/abs/2305.03022 · HTML: https://mlsanity.com/paper/2305.03022/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 29 references that are in the map.
- First posted on arXiv 3 May 2023.

## Builds on (most cited first, 3 of 3)

- [Adjusting for Chance Clustering Comparison Measures](https://mlsanity.com/paper/1512.01286/) (2015)
- [Community detection in node-attributed social networks: a survey](https://mlsanity.com/paper/1912.09816/) (2019)
- [Pairwise Adjusted Mutual Information](https://mlsanity.com/paper/2103.12641/) (2021)

## Built on by (most cited first, 1 of 1)

- [ShadowCoT: Cognitive Hijacking for Stealthy Reasoning Backdoors in LLMs](https://mlsanity.com/paper/2504.05605/) (2025)

## Abstract (arXiv)

Clustering is at the very core of machine learning, and its applications proliferate with the increasing availability of data. However, as datasets grow, comparing clusterings with an adjustment for chance becomes computationally difficult, preventing unbiased ground-truth comparisons and solution selection. We propose FastAMI, a Monte Carlo-based method to efficiently approximate the Adjusted Mutual Information (AMI) and extend it to the Standardized Mutual Information (SMI). The approach is compared with the exact calculation and a recently developed variant of the AMI based on pairwise permutations, using both synthetic and real data. In contrast to the exact calculation our method is fast enough to enable these adjusted information-theoretic comparisons for large datasets while maintaining considerably more accurate results than the pairwise approach.