3 citations · 5 across the 6 of their papers we have counts for
6 papers
Clustering High-dimensional Data: Balancing Abstraction and Representation Tutorial at AAAI 2026
Claudia Plant, Lena G. M. Bauer, Christian Böhm
How to find a natural grouping of a large real data set? Clustering requires a balance between abstraction and representation. To identify clusters, we need to abstract from superf…
Bootstrap Deep Spectral Clustering with Optimal Transport
Wengang Guo, Wei Ye, Chunchun Chen +4
Spectral clustering is a leading clustering method. Two of its major shortcomings are the disjoint optimization process and the limited representation capacity. To address these is…
An Introductory Survey to Autoencoder-based Deep Clustering -- Sandboxes for Combining Clustering with Deep Learning
Collin Leiber, Lukas Miklautz, Claudia Plant +1
Autoencoders offer a general way of learning low-dimensional, non-linear representations from data without labels. This is achieved without making any particular assumptions about…
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…
Temporal Subspace Clustering for Molecular Dynamics Data
Anna Beer, Martin Heinrigs, Claudia Plant +1
We introduce MOSCITO (MOlecular Dynamics Subspace Clustering with Temporal Observance), a subspace clustering for molecular dynamics data. MOSCITO groups those timesteps of a molec…
Extension of the Dip-test Repertoire -- Efficient and Differentiable p-value Calculation for Clustering
Lena G. M. Bauer, Collin Leiber, Christian Böhm +1
Over the last decade, the Dip-test of unimodality has gained increasing interest in the data mining community as it is a parameter-free statistical test that reliably rates the mod…