1. Achtert, E., Böhm, C., Kriegel, H.-P., Kröger, P., Müller-Gorman, I., & Zimek, A. (2006a). Finding hierarchies of subspace clusters. In LNCS: Knowledge discovery in databases. Lecture notes in computer Science (Vol. 4213, pp. 446–453).
2. Achtert, E., Böhm, C., & Kröger, P. (2006b). DeLi-Clu: Boosting robustness, completeness, usability, and efficiency of hierarchical clustering by a closest pair ranking. In LNCS: Advances in knowledge discovery and data mining. Lecture notes in computer science (Vol. 3918, pp. 119–128).
3. Achtert, E., Böhm, C., Kröger, P., & Zimek, A. (2006c). Mining hierarchies of correlation clusters. In Proceedings of the 18th International Conference on Scientific and Statistical Database Management (SSDBM’06) (Vol. 1, pp. 119–128).
4. Ankerst, M., Breunig, M. M., Kriegel, H.-P., & Sander, J. (1999). OPTICS: Ordering points to identify the clustering structure. In Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD/PODS’99) (pp. 49–60), PA,USA.
5. Arthur, D., & Vassilvitskii, S. (2007). k-means++: The advantages of careful seeding. In Proceedings of the 18th Annual ACM-SIAM Symposium on Discrete Algorithms(SIAM’07) (pp. 1027–1035), Philadelphia, PA, USA.