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.

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 

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 

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 

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 

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 

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