Blömer J, Brauer S, Bujna K. A Complexity Theoretical Study of Fuzzy K-Means. ACM Transactions on Algorithms. 2020;16(4):1-25. doi:10.1145/3409385

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

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

Brauer S. Classification and Approximation of Geometric Location Problems. Paderborn; 2019. doi:10.17619/UNIPB/1-816

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

Bujna K. Soft Clustering Algorithms - Theoretical and Practical Improvements. Universität Paderborn; 2017. doi:10.17619/UNIPB/1-226

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

Blömer J, Brauer S, Bujna K. Hard-Clustering with Gaussian Mixture Models. 2016.

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

Blömer J, Brauer S, Bujna K. A Theoretical Analysis of the Fuzzy K-Means Problem. In: 2016 IEEE 16th International Conference on Data Mining (ICDM). IEEE; 2016:805-810. doi:10.1109/icdm.2016.0094

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

Ackermann MR, Blömer J, Kuntze D, Sohler C. Analysis of Agglomerative Clustering. Algorithmica. 2014;69. doi:10.1007/s00453-012-9717-4

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

Kuntze D. Practical Algorithms for Clustering and Modeling Large Data Sets - Analysis and Improvements. Universität Paderborn; 2013.

Ackermann MR, Märtens M, Raupach C, Swierkot K, Lammersen C, Sohler C. StreamKM++: A clustering algorithm for data streams. 2012;17. doi:10.1145/2133803.2184450

Ackermann MR, Blömer J, Scholz C. Hardness and Non-Approximability of Bregman Clustering Problems. 2011.

Ackermann MR, Blömer J. Bregman Clustering for Separable Instances. In: SWAT 2010. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg; 2010:212-223. doi:10.1007/978-3-642-13731-0_21

Ackermann MR, Blömer J, Sohler C. Clustering for Metric and Nonmetric Distance Measures. ACM Trans Algorithms. 2010;(4):59:1--59:26. doi:10.1145/1824777.1824779

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

Ackermann MR. Algorithms for the Bregman K-Median Problem. Universität Paderborn; 2009.