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Klede, K.* ; Altstidl, T.* ; Zanca, D.* ; Eskofier, B.M.

The impact of random models on standardized clustering similarity.

IEEE Access 12, 179879-179890 (2024)
Verlagsversion DOI
Open Access Gold
Creative Commons Lizenzvertrag
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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Clustering Comparison ; External Evaluation Metrics ; Machine Learning ; Mutual Information ; Rand Index ; Random Model
Sprache englisch
Veröffentlichungsjahr 2024
HGF-Berichtsjahr 2024
ISSN (print) / ISBN 2169-3536
e-ISSN 2169-3536
Zeitschrift IEEE Access
Quellenangaben Band: 12, Heft: , Seiten: 179879-179890 Artikelnummer: , Supplement: ,
Verlag IEEE
Verlagsort 445 Hoes Lane, Piscataway, Nj 08855-4141 Usa
Begutachtungsstatus Peer reviewed
Institut(e) Human-Centered AI (HCA)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-540008-001
Förderungen Digital Europe Grant Testing and Experimentation Facility for Health Artificial Intelligence (AI) and Robotics (TEF-Health)
DOI 10.1109/ACCESS.2024.3507133
WOS ID 001373836200040
Scopus ID 85210353853
Erfassungsdatum 2024-12-09
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