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

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 

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., 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.