M. R. Ackermann, Algorithms for the Bregman k-Median Problem. Universität Paderborn, 2009.

M. R. Ackermann and J. Blömer, “Coresets and Approximate Clustering for Bregman Divergences,” in Proceedings of the Twentieth Annual ACM-SIAM Symposium on Discrete Algorithms, Philadelphia, PA: Society for Industrial and Applied Mathematics, 2009, pp. 1088–1097.

M. R. Ackermann and J. Blömer, “Bregman Clustering for Separable Instances,” in SWAT 2010, Berlin, Heidelberg: Springer Berlin Heidelberg, 2010, pp. 212–223.

M. R. Ackermann, J. Blömer, and C. Sohler, “Clustering for Metric and Nonmetric Distance Measures,” ACM Trans. Algorithms, no. 4, pp. 59:1--59:26, 2010.

A. Krueger, V. Leutnant, R. Haeb-Umbach, M. Ackermann, and J. Blömer, “On the initialization of dynamic models for speech features,” Proc. of ITG Fachtagung Sprachkommunikation. ITG, Bochum, Germany, 2010.

M. R. Ackermann, J. Blömer, and C. Scholz, “Hardness and Non-Approximability of Bregman Clustering Problems.” 2011.

M. R. Ackermann, M. Märtens, C. Raupach, K. Swierkot, C. Lammersen, and C. Sohler, “StreamKM++: A clustering algorithm for data streams,” vol. 17. ACM, New York, NY, USA, 2012.

D. Kuntze, Practical algorithms for clustering and modeling large data sets - Analysis and improvements. Universität Paderborn, 2013.

S. Brauer, A Probabilistic Expectation Maximization Algorithm for Multivariate Laplacian Mixtures. 2014.

M. R. Ackermann, J. Blömer, D. Kuntze, and C. Sohler, “Analysis of Agglomerative Clustering,” Algorithmica, vol. 69, 2014.

J. Blömer, K. Bujna, and D. Kuntze, “A Theoretical and Experimental Comparison of the EM and SEM Algorithm,” in 2014 22nd International Conference on Pattern Recognition, 2014.

J. Blömer, S. Brauer, and K. Bujna, “Hard-Clustering with Gaussian Mixture Models.” Computing Research Repository, 2016.

J. Blömer and K. Bujna, “Adaptive Seeding for Gaussian Mixture Models,” in Advances in Knowledge Discovery and Data Mining, Cham: Springer International Publishing, 2016, pp. 296–308.

J. Blömer, S. Brauer, and K. Bujna, “A Theoretical Analysis of the Fuzzy K-Means Problem,” in 2016 IEEE 16th International Conference on Data Mining (ICDM), Barcelona, Spain, 2016, pp. 805–810.

K. Bujna, Soft Clustering Algorithms - Theoretical and Practical Improvements. Universität Paderborn, 2017.

S. Brauer, “Complexity of Single-Swap Heuristics for Metric Facility Location and Related Problems,” in Lecture Notes in Computer Science, vol. 10236, D. Fotakis, A. Pagourtzis, and V. T. Paschos, Eds. Cham: Springer International Publishing, 2017, pp. 116–127.

J. Blömer, S. Brauer, and K. Bujna, “Coresets for Fuzzy K-Means with Applications,” in 29th International Symposium on Algorithms and Computation  (ISAAC 2018), Jiaoxi, Yilan County, Taiwan, 2018, pp. 46:1--46:12.

S. Brauer, “Complexity of single-swap heuristics for metric facility location and related problems,” Theoretical Computer Science, vol. 754, pp. 88–106, 2019.

S. Brauer, Classification and Approximation of Geometric Location Problems. Paderborn, 2019.

J. Blömer, S. Brauer, and K. Bujna, “A Complexity Theoretical Study of Fuzzy K-Means,” ACM Transactions on Algorithms, vol. 16, no. 4, pp. 1–25, 2020.

J. Blömer, S. Brauer, K. Bujna, and D. Kuntze, “How well do SEM algorithms imitate EM algorithms? A non-asymptotic analysis for mixture models,” Advances in Data Analysis and Classification, vol. 14, pp. 147–173, 2020.