Hotegni, S. S., Mahabadi, S., &; Vakilian, A. (n.d.). Approximation Algorithms for Fair Range Clustering. Proceedings of the 40th International Conference on Machine Learning, Honolulu, Hawaii, USA. PMLR 202, 2023. International Conference on Machine Learning, Honolulu, Hawaii, USA.
LibreCat: /record/45695

Muth, L., Noll, C., &; Sextro, W. (2022). Generation of a Reduced, Representative, Virtual Test Drive for Fast Evaluation of Tire Wear by Clustering of Driving Data. In A. Orlova &; D. Cole (Eds.), Advances in Dynamics of Vehicles on Roads and Tracks II - Proceedings of the 27th Symposium of the International Association of Vehicle System Dynamics, IAVSD 2021. Springer.https://doi.org/10.1007/978-3-031-07305-2_92 
LibreCat: /record/29934
DOI: 10.1007/978-3-031-07305-2_92

Aimiyekagbon, O. K., Muth, L., Wohlleben, M. C., Bender, A., &; Sextro, W. (2021). Rule-based Diagnostics of a Production Line. In P. Do, S. King, &; O. Fink (Eds.), Proceedings of the European Conference of the PHM Society 2021 (Vol. 6, Issue 1, pp. 527–536).https://doi.org/10.36001/phme.2021.v6i1.3042 
LibreCat: /record/27111
DOI: 10.36001/phme.2021.v6i1.3042

Blömer, J., Brauer, S., &; Bujna, K. (2016). A Theoretical Analysis of the Fuzzy K-Means Problem. In 2016 IEEE 16th International Conference on Data Mining (ICDM) (pp. 805–810). Barcelona, Spain: IEEE.https://doi.org/10.1109/icdm.2016.0094 
LibreCat: /record/2367
DOI: 10.1109/icdm.2016.0094

Ackermann, M. R., Blömer, J., &; Sohler, C. (2010). Clustering for Metric and Nonmetric Distance Measures. ACM Trans. Algorithms, (4), 59:1--59:26.https://doi.org/10.1145/1824777.1824779 
LibreCat: /record/2990
DOI: 10.1145/1824777.1824779

Kaufmann, P., &; Platzner, M. (2007). MOVES: A Modular Framework for Hardware Evolution. In Second NASA/ESA Conference on Adaptive Hardware and Systems (AHS 2007) (pp. 447–454). Edinburgh, UK: IEEE.https://doi.org/10.1109/ahs.2007.73 
LibreCat: /record/6508
DOI: 10.1109/ahs.2007.73

Haeb-Umbach, R. (2001). Automatic generation of phonetic regression class trees for MLLR adaptation. IEEE Transactions on Speech and Audio Processing, 9(3), 299–302.https://doi.org/10.1109/89.906003 
LibreCat: /record/11778
DOI: 10.1109/89.906003