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.

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, “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.

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, “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.

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.

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

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.

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

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