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.
LibreCat: /record/20888
DOI: 10.1145/3409385

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.
LibreCat: /record/10790
DOI: 10.1007/s11634-019-00366-7

S. Brauer, “Complexity of single-swap heuristics for metric facility location and related problems,” Theoretical Computer Science, vol. 754, pp. 88–106, 2019.
LibreCat: /record/2916
DOI: 10.1016/j.tcs.2018.04.048

S. Brauer, Classification and Approximation of Geometric Location Problems. Paderborn, 2019.
LibreCat: /record/13679
DOI: 10.17619/UNIPB/1-816

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.
LibreCat: /record/4344
DOI: 10.4230/LIPIcs.ISAAC.2018.46

K. Bujna, Soft Clustering Algorithms - Theoretical and Practical Improvements. Universität Paderborn, 2017.
LibreCat: /record/2908
DOI: 10.17619/UNIPB/1-226

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.
LibreCat: /record/2381
DOI: 10.1007/978-3-319-57586-5_11

J. Blömer, S. Brauer, and K. Bujna, “Hard-Clustering with Gaussian Mixture Models.” Computing Research Repository, 2016.
LibreCat: /record/2969

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.
LibreCat: /record/2970
DOI: 10.1007/978-3-319-31750-2_24

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.
LibreCat: /record/2367
DOI: 10.1109/icdm.2016.0094

S. Brauer, A Probabilistic Expectation Maximization Algorithm for Multivariate Laplacian Mixtures. 2014.
LibreCat: /record/2900

M. R. Ackermann, J. Blömer, D. Kuntze, and C. Sohler, “Analysis of Agglomerative Clustering,” Algorithmica, vol. 69, 2014.
LibreCat: /record/2976
DOI: 10.1007/s00453-012-9717-4

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.
LibreCat: /record/2977
DOI: 10.1109/icpr.2014.253

D. Kuntze, Practical algorithms for clustering and modeling large data sets - Analysis and improvements. Universität Paderborn, 2013.
LibreCat: /record/2909

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.
LibreCat: /record/2982
DOI: 10.1145/2133803.2184450

M. R. Ackermann, J. Blömer, and C. Scholz, “Hardness and Non-Approximability of Bregman Clustering Problems.” 2011.
LibreCat: /record/2985

M. R. Ackermann and J. Blömer, “Bregman Clustering for Separable Instances,” in SWAT 2010, Berlin, Heidelberg: Springer Berlin Heidelberg, 2010, pp. 212–223.
LibreCat: /record/2988
DOI: 10.1007/978-3-642-13731-0_21

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.
LibreCat: /record/2990
DOI: 10.1145/1824777.1824779

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.
LibreCat: /record/2991

M. R. Ackermann, Algorithms for the Bregman k-Median Problem. Universität Paderborn, 2009.
LibreCat: /record/2911