Inductive Multi-view Multiple Clusterings

Wei, Shaowei; Han, Guangyang; Wang, Runmin; Yang, Yuanlin; Zhang, Huiling; Li, Sufang

doi:10.1109/bigdia53151.2021.9619704

Cited by 1 publication

(1 citation statement)

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“…In the context of graphs, the mutual information of the neighborhoods of two nodes indicates their similarity and can predict missing links (Hoffman, Steinley, and Brusco 2015;Shakibian and Moghadam Charkari 2017). Another application is multipleclustering algorithms, which measure the mutual information or one of its variants to identify multiple qualitatively different clustering solutions for a single dataset (Müller et al 2010;Wei et al 2021). Other uses include categorical feature selection, where each feature is understood as a cluster or for the solution selection in consensus clustering (Lancichinetti and Fortunato 2012).…”

Section: Introductionmentioning

confidence: 99%

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

Kai

Schwinn

Zanca

et al. 2023

AAAI

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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.

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