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cs.LG2025
Internal Evaluation of Density-Based Clusterings with Noise
Anna Beer, Lena Krieger, Pascal Weber +3
Being able to evaluate the quality of a clustering result even in the absence of ground truth cluster labels is fundamental for research in data mining. However, most cluster valid…
cs.LG2025
Ultrametric Cluster Hierarchies: I Want 'em All!
Andrew Draganov, Pascal Weber, Rasmus Skibdahl Melanchton Jørgensen +3
Hierarchical clustering is a powerful tool for exploratory data analysis, organizing data into a tree of clusterings from which a partition can be chosen. This paper generalizes th…
cs.LG2024
SHADE: Deep Density-based Clustering
Anna Beer, Pascal Weber, Lukas Miklautz +4
Detecting arbitrarily shaped clusters in high-dimensional noisy data is challenging for current clustering methods. We introduce SHADE (Structure-preserving High-dimensional Analys…