FastAMI – a Monte Carlo Approach to the Adjustment for Chance in Clustering Comparison Metrics
Keywords:ML: Clustering, ML: Probabilistic Methods
AbstractClustering 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.
How to Cite
Klede, K., Schwinn, L., Zanca, D., & Eskofier, B. (2023). FastAMI – a Monte Carlo Approach to the Adjustment for Chance in Clustering Comparison Metrics. Proceedings of the AAAI Conference on Artificial Intelligence, 37(7), 8317-8324. https://doi.org/10.1609/aaai.v37i7.26003
AAAI Technical Track on Machine Learning II