Atzmueller, M., Lemmerich, F., Krause, B. & Hotho, A. (2009). Who are the Spammers? Understandable Local Patterns for Concept Description. 7th Conference on Computer Methods and Systems, November, Krakow, Poland.

(2000). Web Usage Analysis and User Profiling, International WEBKDD'99
Workshop, San Diego, California, USA, August 15, 1999, Revised
Papers . In B. M. Masand & M. Spiliopoulou (ed.), WEBKDD. Springer

Hotho, A., Ulslev Pedersen, R. & Wurst, M. (2010). Ubiquitous Data. Lecture Notes in Computer Science, , 61--74.

Atzmueller, M., Lemmerich, F., Krause, B. & Hotho, A. (2009). Towards Understanding Spammers - Discovering Local Patterns for Concept Characterization and Description. In J. F. A. Knobbe (ed.), Proc. LeGo-09: From Local Patterns to Global Models, Workshop at the 2009 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, .

Baeza-Yates, R., Calderón-Benavides, L. & González-Caro, C. (2006). The Intention Behind Web Queries. String Processing and Information Retrieval, , 98--109.

Krause, B., Schmitz, C., Hotho, A. & Stumme, G. (2008). The Anti-Social Tagger - Detecting Spam in Social Bookmarking Systems. AIRWeb '08: Proceedings of the 4th international workshop on Adversarial information retrieval on the web (p./pp. 61--68), New York, NY, USA: ACM. ISBN: 978-1-60558-159-0

Weiss, S. M., Indurkhya, N.,, Zhang, T. (2004). Text Mining. Predictive Methods for Analyzing Unstructured Information. Springer, Berlin. ISBN: 0387954333

Bullock, B. N., Jäschke, R. & Hotho, A. (2011). Tagging data as implicit feedback for learning-to-rank. Proceedings of the ACM WebSci'11, June, .

Thrun, S., Burgard, W.,, Fox, D. (2001). Probabilistic Robotics (Intelligent Robotics and Autonomous Agents).

Flajolet, P. & Martin, G. N. (1985). Probabilistic Counting Algorithms for Data Base Applications. Journal of Computer and System Sciences, 31, 182-209.

Buitelaar, P., Cimiano, P. & Magnini, B. (eds.) (2005). Ontology Learning from Text: Methods, Evaluation and Applications (Vol. 123). IOS Press.

Baldi, P., Frasconi, P. & Smyth, P. (2003). Modeling the Internet and the Web: Probabilistic Methods and Algorithms. Modeling the Internet and the Web: Probabilistic Methods and Algorithms. Wiley.

Song, C., Qu, Z., Blumm, N. & Barabási, A.-L. (2010). Limits of Predictability in Human Mobility. Science, 327, 1018-1021. doi: 10.1126/science.1177170

Morstatter, F., ürgen Pfeffer, J., Liu, H. & Carley, K. M. (2013). Is the Sample Good Enough? Comparing Data from Twitter’s Streaming API with Twitter’s Firehose. , .

Berendt, B., Hotho, A., Mladenic, D. & Semeraro, G. (eds.) (2007). From Web to Social Web: Discovering and Deploying User and Content Profiles (Vol. 4736). Springer. ISBN: 978-3-540-74950-9

Fayyad, U. M., Piatetsky-Shapiro, G. & Smyth, P. (1996). From Data Mining to Knowledge Discovery: An Overview.. In Advances in Knowledge Discovery and Data Mining (pp. 1-34) . .

Wurst, M. & Morik, K. (2007). Distributed feature extraction in a p2p setting: a case study. Future Gener. Comput. Syst., 23, 69--75. doi: http://dx.doi.org/10.1016/j.future.2006.04.004

Balakrishnan, H. & Deo, N. (2006). Discovering communities in complex networks.. In R. Menezes (ed.), ACM Southeast Regional Conference (p./pp. 280-285), : ACM. ISBN: 1-59593-315-8

Pyle, D. (1999). Data Preparation for Data Mining. Morgan Kaufmann. ISBN: 1-55860-529-0

Dhillon, I. S., Modha, D. S. & Spangler, W. S. (2002). Class visualization of high-dimensional data with applications. Computational Statistics & Data Analysis, 41, 59-90.