Data Mining Group – TU Dortmund

Our new article on efficient clustering using graph-based search algorithms has been published in the renowned journal Information Systems:

Camilla Birch Okkels, Erik Thordsen, Martin Aumüller, Arthur Zimek and Erich Schubert.
Approximate hierarchical density-based clustering using graph-based search indexes
In: Information Systems, 102768, 2026. in press.

Andreas Lang successfully defended his PhD thesis on the subject Accelerating Clustering Algorithms with Tree Data Structures.

The Stanford statistician John Ioannidis publishes a list of the 100,000 most influential scientists (science-wide).

In the single-year ranking of the current version (based on the data for the year 2024, published October 2025), Prof. Schubert improves to rank 28048 across all disciplines, up from 41274 (2023), 62210 (2022) und 92735 (2021)

Best paper award for Erik Thordsen and Erich Schubert, Theoretical and Practical Insights into Graph-Based Indexing at the 18th International Conference on Similarity Search and Applications (SISAP) in Reykjavik, Iceland.

In 2025 we so far published seven new papers in Springer Data Mining and Knowledge Discovery, Elsevier Information Systems, the HICSS and the SISAP conference.

Erik Thordsen successfully defended his PhD thesis on the subject Analysis of High-Dimensional Data in the Context of Intrinsic Dimensionality.

Lars Lenssen successfully defended his PhD thesis on the subject Measuring and Resource-Efficient Optimization of Clustering Quality today.

• Archetype Discovery from Taxonomies: A Method to Cluster Small Datasets of Categorical Data, Lars Lenssen, Philip Stahmann, Christian Janiesch, Erich Schubert, 58th Hawaii Int. Conf. on System Sciences (HICSS), best paper nomination
• Hierarchical Clustering Without Pairwise Distances by Incremental Similarity Search, Erich Schubert,Int. Conf. on Similarity Search and Applications, SISAP, 2024
• Advancing the PAM Algorithm to Semi-supervised k-Medoids Clustering, Miriama Jánošová, Andreas Lang, Petra Budikova, Erich Schubert, Vlastislav Dohnal, Int. Conf. on Similarity Search and Applications, SISAP, 2024
• Grouping Sketches to Index High-Dimensional Data in a Resource-Limited Setting, Erik Thordsen, Erich Schubert, Int. Conf. on Similarity Search and Applications, SISAP, 2024

Our group received three awards in one week:

• best student paper award at SISAP 2023 for a paper improving sketch-based similarity search in high-dimensional data: Erik Thordsen and Erich Schubert. An Alternating Optimization Scheme for Binary Sketches for Cosine Similarity Search
• best paper award at SISAP 2023 for the same contribution
• best paper award of the SIG Knowledge Discovery, Data Mining and Machine Learning (KDML) at the LWDA 2023 for improving nearest-neighbor consistent clustering: Lars Lenssen, Niklas Strahmann and Erich Schubert. Fast k-Nearest-Neighbor-Consistent Clustering

The following new publications were accepted:

• Lars Lenssen and Erich Schubert. Medoid Silhouette clustering with automatic cluster number selection. In: Information Systems 120, 102290, 2024 (online 2023)
• Lars Lenssen, Niklas Strahmann and Erich Schubert. Fast k-Nearest-Neighbor-Consistent Clustering In: Proceedings of the Conference "Lernen, Wissen, Daten, Analysen" (LWDA), Marburg, Germany, 2023, to appear. best paper award
• Melanie Derksen et al. Who Did What When? Discovering Complex Historical Interrelations in Immersive Virtual Reality In: 22nd IEEE International Symposium on Mixed and Augmented Reality (ISMAR), 2023, to appear.