Statistical Analysis of Agreement between DBSCAN and HDBSCAN Clustering Results
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We propose a statistical approach to comparing the DBSCAN and HDBSCAN clus-
tering algorithms. The approach examines transitions in object status (core, border, or noise) and
the association between clustering results. The neighborhood radius ε for DBSCAN is selected
subject to a constraint defined by the proportion of objects labeled as noise by HDBSCAN. The
resulting assignments are analyzed using contingency tables, Pearson’s χ 2 statistic, CramГ©r’s V ,
and Tschuprow’s T. A computational experiment is conducted on data with clusters of differing
densities. The two algorithms identify nearly identical numbers of noise objects. CramГ©r’s V
increases with the number of nearest neighbors k, whereas Tschuprow’s T shows an overall increase
but is not monotonic. The results support the use of HDBSCAN-derived information for calibrating
DBSCAN and for a statistical comparison of density-based clustering results.
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