| Connecting the Dots--Density-Connectivity Distance unifies DBSCAN, k-Center and Spectral Clustering A Beer, A Draganov, E Hohma, P Jahn, CMM Frey, I Assent Proceedings of the 29th ACM SIGKDD conference on knowledge discovery and …, 2023 | 32 | 2023 |
| Over-optimistic evaluation and reporting of novel cluster algorithms: an illustrative study T Ullmann, A Beer, M Hünemörder, T Seidl, AL Boulesteix Advances in Data Analysis and Classification 17 (1), 211-238, 2023 | 20 | 2023 |
| SCAR-spectral clustering accelerated and robustified EH Ellen, CMM Frey, A Beer, T Seidl Proceedings of the VLDB Endowment 15 (11), 3031-3044, 2022 | 18 | 2022 |
| A Generator for Subspace Clusters. A Beer, NS Schüler, T Seidl LWDA, 69-73, 2019 | 13 | 2019 |
| Fatbird: A tool for flight and trajectories analyses of birds D Kazempour, A Beer, F Herzog, D Kaltenthaler, JY Lohrer, T Seidl 2018 IEEE 14th International Conference on e-Science (e-Science), 75-82, 2018 | 9 | 2018 |
| MORe++: k-Means Based Outlier Removal on High-Dimensional Data A Beer, J Lauterbach, T Seidl International Conference on Similarity Search and Applications, 188-202, 2019 | 8 | 2019 |
| Data with density-based clusters: A generator for systematic evaluation of clustering algorithms P Jahn, CMM Frey, A Beer, C Leiber, T Seidl Joint European Conference on Machine Learning and Knowledge Discovery in …, 2024 | 6 | 2024 |
| KISS-A fast kNN-based Importance Score for Subspaces. A Beer, E Allerborn, V Hartmann, T Seidl EDBT, 391-396, 2021 | 6* | 2021 |
| Angle-based clustering A Beer, D Seeholzer, NS Schüler, T Seidl International Conference on Similarity Search and Applications, 312-320, 2020 | 6 | 2020 |
| LUCK-linear correlation clustering using cluster algorithms and a kNN based distance function A Beer, D Kazempour, L Stephan, T Seidl Proceedings of the 31st International Conference on Scientific and …, 2019 | 6 | 2019 |
| Rock–Let the points roam to their clusters themselves A Beer, D Kazempour, T Seidl Proceedings of the 22nd International Conference on Extending Database …, 2019 | 5* | 2019 |
| PARADISO: an interactive approach of parameter selection for the mean shift algorithm D Kazempour, A Beer, JY Lohrer, D Kaltenthaler, T Seidl Proceedings of the 30th International Conference on Scientific and …, 2018 | 5 | 2018 |
| Shade: Deep density-based clustering A Beer, P Weber, L Miklautz, C Leiber, W Durani, C Böhm, C Plant 2024 IEEE International Conference on Data Mining (ICDM), 675-680, 2024 | 4 | 2024 |
| I fold you so! An internal evaluation measure for arbitrary oriented subspace clustering D Kazempour, A Beer, P Kröger, T Seidl 2020 International Conference on Data Mining Workshops (ICDMW), 316-323, 2020 | 4 | 2020 |
| Chain-detection for DBSCAN J Held, A Beer, T Seidl BTW 2019–Workshopband, 173-183, 2019 | 4 | 2019 |
| FairDen: Fair Density-Based Clustering L Krieger, A Beer, P Matthews, AM Thiesson, I Assent The Thirteenth International Conference on Learning Representations, 2025 | 3 | 2025 |
| Cluster Flow-an Advanced Concept for Ensemble-Enabling, Interactive Clustering S Obermeier, A Beer, F Wahl, T Seidl BTW 2021, 175-194, 2021 | 3 | 2021 |
| Grace-Limiting the Number of Grid Cells for Clustering High-Dimensional Data. A Beer, D Kazempour, J Busch, A Tekles, T Seidl LWDA, 11-22, 2020 | 3 | 2020 |
| Graph Ordering and Clustering: A Circular Approach A Beer, T Seidl Proceedings of the 31st International Conference on Scientific and …, 2019 | 3 | 2019 |
| LUCKe—Connecting Clustering and Correlation Clustering A Beer, L Stephan, T Seidl 2021 International Conference on Data Mining Workshops (ICDMW), 431-440, 2021 | 2 | 2021 |
Thomas SeidlProfessor of Computer Science, LMU Munich, Munich Center for Machine Learning (MCML)Verified email at dbs.ifi.lmu.de
