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Blömer, J., Brauer, S., Bujna, K., &; Kuntze, D. (2020). How well do SEM algorithms imitate EM algorithms? A non-asymptotic analysis for mixture models. Advances in Data Analysis and Classification, 14, 147–173.https://doi.org/10.1007/s11634-019-00366-7 

Brauer, S. (2019). Complexity of single-swap heuristics for metric facility location and related problems. Theoretical Computer Science, 754, 88–106.https://doi.org/10.1016/j.tcs.2018.04.048 

Brauer, S. (2019). Classification and Approximation of Geometric Location Problems. Paderborn.https://doi.org/10.17619/UNIPB/1-816 

Blömer, J., Brauer, S., &; Bujna, K. (2018). Coresets for Fuzzy K-Means with Applications. In 29th International Symposium on Algorithms and Computation  (ISAAC 2018) (pp. 46:1--46:12). Jiaoxi, Yilan County, Taiwan: Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik.https://doi.org/10.4230/LIPIcs.ISAAC.2018.46 

Bujna, K. (2017). Soft Clustering Algorithms - Theoretical and Practical Improvements. Universität Paderborn.https://doi.org/10.17619/UNIPB/1-226 

Brauer, S. (2017). Complexity of Single-Swap Heuristics for Metric Facility Location and Related Problems. In D. Fotakis, A. Pagourtzis, &; V. T. Paschos (Eds.), Lecture Notes in Computer Science (Vol. 10236, pp. 116–127). Cham: Springer International Publishing.https://doi.org/10.1007/978-3-319-57586-5_11 

Blömer, J., Brauer, S., &; Bujna, K. (2016). Hard-Clustering with Gaussian Mixture Models. Computing Research Repository.

Blömer, J., &; Bujna, K. (2016). Adaptive Seeding for Gaussian Mixture Models. In Advances in Knowledge Discovery and Data Mining (pp. 296–308). Cham: Springer International Publishing.https://doi.org/10.1007/978-3-319-31750-2_24 

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 

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

Ackermann, M. R., Blömer, J., Kuntze, D., &; Sohler, C. (2014). Analysis of Agglomerative Clustering. Algorithmica, 69.https://doi.org/10.1007/s00453-012-9717-4 

Blömer, J., Bujna, K., &; Kuntze, D. (2014). A Theoretical and Experimental Comparison of the EM and SEM Algorithm. In 2014 22nd International Conference on Pattern Recognition. IEEE.https://doi.org/10.1109/icpr.2014.253 

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

Ackermann, M. R., Märtens, M., Raupach, C., Swierkot, K., Lammersen, C., &; Sohler, C. (2012). StreamKM++: A clustering algorithm for data streams. New York, NY, USA: ACM.https://doi.org/10.1145/2133803.2184450 

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Ackermann, M. R., &; Blömer, J. (2010). Bregman Clustering for Separable Instances. In SWAT 2010 (pp. 212–223). Berlin, Heidelberg: Springer Berlin Heidelberg.https://doi.org/10.1007/978-3-642-13731-0_21 

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 

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

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