At the SISAP 2022 conference at the University of Bologna, Lars Lenssen (SFB876, Project A2) won the "best student paper" award for the contribution "Lars Lenssen, Erich Schubert. Clustering by Direct Optimization of the Medoid Silhouette. In: Similarity Search and Applications. SISAP 2022. https://doi.org/10.1007/978-3-031-17849-8_15".
The publisher Springer donates a monetary prize for the awards, and the best contributions are invited to submit an extended version to a special issue of the A* journal "Information Systems".
In this paper, we introduce a new clustering method that directly optimizes the Medoid Silhouette, a variant of the popular Silhouette measure of clustering quality. As the new variant is O(k²) times faster than previous approaches, we can cluster data sets larger by orders of magnitude, where large values of k are desirable. The implementation is available in the Rust "kmedoids" crate and the Python module "kmedoids", the code is open source on Github.

The group is successful for the second time: In 2020, Erik Thordsen won the award with the contribution "Erik Thordsen, Erich Schubert. ABID: Angle Based Intrinsic Dimensionality. In: Similarity Search and Applications. SISAP 2020. https://doi.org/10.1007/978-3-030-60936-8_17".
This paper introduced a new angle-based estimator of the intrinsic dimensionality – a measure of local data complexity – traditionally estimated solely from distances.