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

p-value Adjustment for Monotonous, Unbiased, and Fast Clustering Comparison.

In: (37th Conference on Neural Information Processing Systems (NeurIPS), 10-16 December 2023, New Orleans, LA). 10010 North Torrey Pines Rd, La Jolla, California 92037 Usa: Neural Information Processing Systems (nips), 2023. 16
Popular metrics for clustering comparison, like the Adjusted Rand Index and the Adjusted Mutual Information, are type II biased. The Standardized Mutual Information removes this bias but suffers from counterintuitive non-monotonicity and poor computational efficiency. We introduce the p-value adjusted Rand Index (PMI2), the first cluster comparison method that is type II unbiased and provably monotonous. The PMI2 has fast approximations that outperform the Standardized Mutual information. We demonstrate its unbiased clustering selection, approximation quality, and runtime efficiency on synthetic benchmarks. In experiments on image and social network datasets, we show how the PMI2 can help practitioners choose better clustering and community detection algorithms.
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Publikationstyp Artikel: Konferenzbeitrag
Korrespondenzautor
ISSN (print) / ISBN 1049-5258
Konferenztitel 37th Conference on Neural Information Processing Systems (NeurIPS)
Konferzenzdatum 10-16 December 2023
Konferenzort New Orleans, LA
Quellenangaben Band: , Heft: , Seiten: 16 Artikelnummer: , Supplement: ,
Verlag Neural Information Processing Systems (nips)
Verlagsort 10010 North Torrey Pines Rd, La Jolla, California 92037 Usa
Nichtpatentliteratur Publikationen
Institut(e) Institute of AI for Health (AIH)
Förderungen Bayern Innovativ - Bayerische Gesellschaft fur Innovation und Wissenstransfer mbH
Bayerischen Verbundforderprogramm (BayVFP) - Forderlinie Digitalisierung -Forderbereich Informations-und Kommunikationstechnik of the Bavarian Ministry of Economic Affairs, Regional Development and Energy