| Erscheinungsdatum | Titel | Autor(en) |
| 2021 | Knowledge-Augmented Induction of Complex Networks on Supply–Demand–Material Data | Hudson, D.; Schwenke, L.; Bloemheuvel, S.; Chowdhury, A.G.; Schut, N.; Atzmueller, M. |
| 2021 | Local Exceptionality Detection in Time Series Using Subgroup Discovery: An Approach Exemplified on Team Interaction Data | Hudson, D.; Wiltshire, T.J.; Atzmueller, M. |
| 2022 | Methods for explaining Top-N recommendations through subgroup discovery | Iferroudjene, M.; Lonjarret, C.; Robardet, C.; Plantevit, M.; Atzmueller, M. |
| 2022 | Predicting User Dropout fromTheir Online Learning Behavior | Shayan, P.; van Zaanen, M.; Atzmueller, M. |
| 2020 | Proceedings of the 1st international workshop on explainable and interpretable machine learning (XI-ML) | Atzmueller, M.; Kliegr, T.; Schmid, U. |
| 2021 | Semantic Data Mining: A Brief Outline | Atzmueller, M.; Nalepa, G.J.; Bobek, S.; Lavrac, N. |
| 2022 | Towards Tabular Data Extraction From Richly-Structured Documents Using Supervised and Weakly-Supervised Learning | Ghosh Chowdhury, A.; Ben Ahmed, M.; Atzmueller, M. |
| 2022 | Visual Detection of Tiny and Transparent Objects for Autonomous Robotic Pick-and-Place Operations | Markert, T.; Matich, S.; Neykov, D.; Muenig, M.; Theissler, A.; Atzmueller, M. |
| 2022 | Visualization Methods forExploratory Subgroup Discovery onTime Series Data | Hudson, D.; Wiltshire, T.J.; Atzmueller, M. |
HOCHSCHULBIBLIOGRAPHIE