Hotegni, Sedjro Salomon, et al. “Approximation Algorithms for Fair Range Clustering.” Proceedings of the 40th International Conference on Machine Learning, Honolulu, Hawaii, USA. PMLR 202, 2023.

Muth, Lars, et al. “Generation of a Reduced, Representative, Virtual Test Drive for Fast Evaluation of Tire Wear by Clustering of Driving Data.”10.1007/978-3-031-07305-2_92__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, edited by Anna Orlova and David Cole, Springer, 2022, doi:10.1007/978-3-031-07305-2_92.

Aimiyekagbon, Osarenren Kennedy, et al. “Rule-Based Diagnostics of a Production Line.”10.36001/phme.2021.v6i1.3042__Proceedings of the European Conference of the PHM Society 2021, edited by Phuc Do et al., vol. 6, no. 1, 2021, pp. 527–36, doi:10.36001/phme.2021.v6i1.3042.

Blömer, Johannes, et al. “A Theoretical Analysis of the Fuzzy K-Means Problem.”10.1109/icdm.2016.0094__2016 IEEE 16th International Conference on Data Mining (ICDM), IEEE, 2016, pp. 805–10, doi:10.1109/icdm.2016.0094.

Ackermann, Marcel R., et al. “Clustering for Metric and Nonmetric Distance Measures.”10.1145/1824777.1824779__ACM Trans. Algorithms, no. 4, 2010, pp. 59:1--59:26, doi:10.1145/1824777.1824779.

Kaufmann, Paul, and Marco Platzner. “MOVES: A Modular Framework for Hardware Evolution.”10.1109/ahs.2007.73__Second NASA/ESA Conference on Adaptive Hardware and Systems (AHS 2007), IEEE, 2007, pp. 447–54, doi:10.1109/ahs.2007.73.

Haeb-Umbach, Reinhold. “Automatic Generation of Phonetic Regression Class Trees for MLLR Adaptation.”10.1109/89.906003__IEEE Transactions on Speech and Audio Processing, vol. 9, no. 3, 2001, pp. 299–302, doi:10.1109/89.906003.