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

Klede K, Schwinn L, Zanca D, Eskofier B (2023)

Publication Language: English

Publication Status: Accepted

Publication Type: Conference contribution, Original article

Future Publication Type: Conference contribution

Publication year: 2023

Series: Proceedings of the AAAI Conference on Artificial Intelligence

Book Volume: 37

Pages Range: 8317-8324

Conference Proceedings Title: Proceedings of the AAAI Conference on Artificial Intelligence, 37(7)

Event location: Washington, D.C. US

URI: https://ojs.aaai.org/index.php/AAAI/article/view/26003

DOI: 10.1609/aaai.v37i7.26003

Open Access Link: https://ojs.aaai.org/index.php/AAAI/article/view/26003

Abstract

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.

Authors with CRIS profile

Kai Klede Machine Learning and Data Analytics Lab Leo Schwinn Machine Learning and Data Analytics Lab Dario Zanca Machine Learning and Data Analytics Lab Björn Eskofier Machine Learning and Data Analytics Lab

How to cite

APA:

Klede, K., Schwinn, L., Zanca, D., & Eskofier, B. (2023). FastAMI - A Monte Carlo Approach to the Adjustment for Chance in Clustering Comparison Metrics. In Proceedings of the AAAI Conference on Artificial Intelligence, 37(7) (pp. 8317-8324). Washington, D.C., US.

MLA:

Klede, Kai, et al. "FastAMI - A Monte Carlo Approach to the Adjustment for Chance in Clustering Comparison Metrics." Proceedings of the AAAI 2023, Washington, D.C. 2023. 8317-8324.

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