Arthur, David ; Vassilvitskii, Sergei: k-means++: the advantages of careful seeding. In: SODA '07: Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms. Philadelphia, PA, USA : Society for Industrial and Applied Mathematics, 2007. - ISBN 978-0-898716-24-5, S. 1027--1035
Bickel, S. ; Scheffer, T.: Multi--View Clustering. In: Proceedings of the IEEE International Conference on Data Mining, 2004
Savaresi, Sergio M. ; Boley, Daniel: A comparative analysis on the bisecting K-means and the PDDP clustering algorithms.. In: Intell. Data Anal., 8 (2004), Nr. 4, S. 345-362
Hotho, A ; Staab, S. ; Stumme, G.: Wordnet improves text document clustering. In: Proc. SIGIR Semantic Web Workshop. Toronto, 2003
Zhao, Ying ; Karypis, George: Evaluation of hierarchical clustering algorithms for document datasets. In: CIKM '02: Proceedings of the eleventh international conference on Information and knowledge management. New York, NY, USA : ACM Press, 2002. - ISBN 1-58113-492-4, S. 515--524
Hotho, Andreas ; Maedche, Alexander ; Staab, Steffen: Text Clustering Based on Good Aggregations. In: ICDM '01: Proceedings of the 2001 IEEE International Conference on Data Mining. Washington, DC, USA : IEEE Computer Society, 2001. - ISBN 0-7695-1119-8, S. 607--608
Wagstaff, Kiri ; Cardie, Claire ; Rogers, Seth ; Schrödl, Stefan: Constrained K-means Clustering with Background Knowledge.. In: ICML, 2001, S. 577-584
Kirsten, Mathias ; Wrobel, Stefan ; Cussens, James (Bearb.) ; Frisch, Alan M. (Bearb.): Extending K-Means Clustering to First-Order Representations. 1866. In: Inductive Logic Programming, 10th International Conference, ILP 2000, London, UK, July 24-27, 2000, Proceedings : Springer, 2000 (Lecture Notes in Computer Science). - ISBN 3-540-67795-X, S. 112-129
Pelleg, Dan ; Moore, Andrew: ${X}$-means: {E}xtending ${K}$-means with Efficient Estimation of the Number of Clusters. In: Proc. 17th International Conf. on Machine Learning : Morgan Kaufmann, San Francisco, CA, 2000, S. 727--734
Steinbach, M. ; Karypis, G. ; Kumar, V.: A comparison of document clustering techniques. In: KDD Workshop on Text Mining, 2000
Steinbach, M. ; Karypis, G. ; Kumar, V.: A Comparison of Document Clustering Techniques. In: KDD Workshop on Text Mining, 2000
Pelleg, Dan ; Moore, Andrew: Accelerating Exact k -means Algorithms with Geometric Reasoning. In: Knowledge Discovery and Data Mining, 1999, S. 277-281
Bradley, Paul S. ; Fayyad, Usama M.: Refining initial points for {K}-{M}eans clustering. In: Proc. 15th International Conf. on Machine Learning : Morgan Kaufmann, San Francisco, CA, 1998, S. 91--99
Hartigan, J.: Clustering Algorithms : John Wiley and Sons, New York, 1975
MacQueen, J. B. ; Cam, L. M. Le (Bearb.) ; Neyman, J. (Bearb.): Some Methods for Classification and Analysis of MultiVariate Observations. 1. In: Proc. of the fifth Berkeley Symposium on Mathematical Statistics and Probability : University of California Press, 1967, S. 281-297
Ball, G. ; Hall, D. ; Stanford Research Institute (Hrsg.): ISODATA: A novel method of data analysis and pattern classification. Menlo Park, 1965
Forgy, E.: Cluster Analysis of Multivariate Data: Efficiency versus Interpretability of Classification. In: Biometrics, 21 (1965), Nr. 3, S. 768-769