ArticleScientific reports2024
Proof of biased behavior of Normalized Mutual Information.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed.
- Telecommunication-inspired network models of healthy and diseased brains.Scientific reports · 2026Article
- A hybrid neighborhood enhanced contrastive learning and self-knowledge distillation method for scRNA-seq data clustering analysis.Bioinformatics (Oxford, England) · 2026Article
- Graph autoencoders and community detection algorithms to improve polymorphic identification.Biology methods & protocols · 2026Article
- MMSpa is a deep learning-based tool that enhances the identification of spatial domains in spatial transcriptomics studies.PLoS biology · 2026Article
- FLACON: An Information-Theoretic Approach to Flag-Aware Contextual Clustering for Large-Scale Document Organization.Entropy (Basel, Switzerland) · 2025Article
- Triple-effect correction for Cell Painting data with contrastive and domain-adversarial learning.Nature communications · 2025Article
- scGGC: a two-stage strategy for single-cell clustering through cellular gene pathway construction.Briefings in bioinformatics · 2025Article
- A novel MIR imaging approach for precise detection ofBiofilm · 2025Review
- Unsupervised multiscale clustering of single-cell transcriptomes to identify hierarchical structures of cell subtypes.GigaScience · 2025Article
- Research on Division of Labor Decision and System Stability of Swarm Robots Based on Mutual Information.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
The Normalized Mutual Information (NMI) metric is widely utilized in the evaluation of clustering and community detection algorithms. This study explores the performance of NMI, specifically examining its performance in relation to the quantity of communities, and uncovers a significant drawback associated with the metric's behavior as the number of communities increases. Our findings reveal a pronounced bias in the NMI as the number of communities escalates. While previous studies have noted this biased behavior, they have not provided a formal proof and have not addressed the causation of this problem, leaving a gap in the existing literature. In this study, we fill this gap by employing a mathematical approach to formally demonstrate why NMI exhibits biased behavior, thereby establishing its unsuitability as a metric for evaluating clustering and community detection algorithms. Crucially, our study exposes the vulnerability of entropy-based metrics that employ logarithmic functions to similar bias.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.