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The impact of random models on standardized clustering similarity.
IEEE Access 12, 179879-179890 (2024)
Clustering similarity measures are essential for evaluating clustering results and ensuring diversity in multiple clusterings of the same dataset. Common indices like the Mutual Information (MI) and Rand Index (RI) are biased towards smaller clusters and are often adjusted using a random permutation model. Recent advancements have standardized these measures to further correct biases, but the impact of different random models on these standardized measures has not yet been studied. In this work, we introduce equations for standardizing the MI/RI under non-permutation models, specifically focusing on a uniform model over all clusterings and a model that fixes the number of clusterings. Our results show that while standardization improves performance for the fixed number of clusters model, its benefits are limited in the more general uniform model. We validate our findings with gene expression data, highlighting the importance of choosing the right similarity metric for clustering comparison.
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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Clustering Comparison ; External Evaluation Metrics ; Machine Learning ; Mutual Information ; Rand Index ; Random Model
Language
english
Publication Year
2024
HGF-reported in Year
2024
ISSN (print) / ISBN
2169-3536
e-ISSN
2169-3536
Journal
IEEE Access
Quellenangaben
Volume: 12,
Pages: 179879-179890
Publisher
IEEE
Publishing Place
445 Hoes Lane, Piscataway, Nj 08855-4141 Usa
Reviewing status
Peer reviewed
Institute(s)
Institute of AI for Health (AIH)
POF-Topic(s)
30205 - Bioengineering and Digital Health
Research field(s)
Enabling and Novel Technologies
PSP Element(s)
G-540008-001
Grants
Digital Europe Grant Testing and Experimentation Facility for Health Artificial Intelligence (AI) and Robotics (TEF-Health)
WOS ID
001373836200040
Scopus ID
85210353853
Erfassungsdatum
2024-12-09