Stefan Rüping

Email: stefan.rueping

iais.fraunhofer.de
Affiliation: Fraunhofer Institute IAIS, Knowledge Discovery Group, Integrated Data Mining Team

About

Stefan Rüping studied computer science at the University of Dortmund from 1994 to 1999. From October 1999 until September 2005 he was a research assistant and Ph. D. student at the Artificial Intelligence Unit of the University of Dortmund, where he was working in the Collaborative Research Center on Complexity Reduction in Multivariate Data (SFB475). In 2006, he finished his Ph. D. as a student of Prof. Katharina Morik of the University of Dortmund. Since October 2005 he is a member of the Knowledge Discovery Group at the Fraunhofer Institute IAIS, where he is currently leading the Integrated Data Mining Group.

Research Interests

Machine Learning and Statistics
Knowledge Discovery and Data Mining
Understandable Models
Distributed Data Mining

Projects

ACGT- Advancing Clinico-Genomic Trials on Cancer
iWebCare
RACWeB

Software

GridR

Publications

Tagaris, A. and Konnis, G. and Benetou, X. and Dimakopoulos, T. and Kassis, K. and Athanasiadis, N. and Rüping, S. and Grosskreutz, H. and Koutsouris, D.: Integrated Web Services Platform for the facilitation of fraud detection in health care e-government services. In: Proceedings of the 9th International Conference on Information Technology and Applications in Biomedicine (ITAB 2009), Lacarna, Cyprus, 2009.
Grosskreutz, Henrik and Rüping, Stefan: On Subgroup Discovery in Numerical Domains. To appear in: Data Mining and Knowledge Discovery, 2009.
Rüping, Stefan: Ranking Interesting Subgroups. In: Proceedings of the 26th International Conference on Machine Learning (ICML 2009), Bottou, Leon and Littman, Michael (Eds.), Montreal, Omnipress, 913-920, 2009.
Seth, Shivani, Rüping, Stefan, and Wrobel, Stefan: Metadata Extraction Using Text Mining. In: Healthgrid Research, Innovation and Business Case - Proceedings of HealthGrid 2009, Solomonides, Tony, Hofmann-Apitius, Martin, Freudigmann, Mathias, Semler, Sebastian Claudius, Legre, Yannick, and Kratz, Mary (eds.), IOS Press, 95-104, 2009.
Rüping, Stefan, Wegener, Dennis, Sfakianakis, Stelios and Sengstag, Thierry: Workflows for Intelligent Monitoring Using Proxy Services. In: Healthgrid Research, Innovation and Business Case - Proceedings of HealthGrid 2009, Solomonides, Tony, Hofmann-Apitius, Martin, Freudigmann, Mathias, Semler, Sebastian Claudius, Legre, Yannick, and Kratz, Mary (eds.), IOS Press, 277-282, 2009.
Wegener, Dennis, Sengstag, Thierry, Sfakianakis, Stelios, Rüping, Stefan, Assi, Anthony: GridR: An R-based tool for scientific data analysis in grid environments. In: Future Generation Computer Systems: The International Journal of Grid Computing: Theory, Methods and Applications, 25(4), 481-488, 2009.
Sfakianakis, Stelios, Graf, Norbert, Hoppe, Aleaxander, Rueping, Stefan, Wegener, Dennis, and Koumakis, Lefteris: Building a System for Advancing Clinico-Genomic Trials on Cancer. In: Bassiliades, N. (ed.), Proceedings of the Workshops of the 5th IFIP Conference on Artificial Intelligence Applications & Innovations (AIAI-2009), Thessaloniki, Greece, April 23-25, pp. 36-47, 2009.
Rüping, Stefan. Globalization of Local Models with SVMs. In: LeGo-08 - From Local Patterns to Global Models, Workshop at ECML/PKDD 08, 2008.
Grosskreutz, Henrik, Rüping, Stefan and Wrobel, Stefan. Tight Optimistic Estimates for Fast Subgroup Discovery. In: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Springer LNAI, 2008.
Rüping, Stefan, Punko, Natalja, Günter, Björn and Grosskreutz, Henrik. Procurement Fraud Discovery using Similarity Measure Learning. In: Transactions on Case-based Reasoning, 1(1), 37-46, 2008.
Rüping, Stefan, Punko, Natalja, Günter, Björn and Grosskreutz, Henrik. Procurement Fraud Discovery using Similarity Measure Learning. In: Advances in Data Mining - Poster and Workshop Proceedings of the 8th Industrial Conference on Data Mining, I. Bechindaritz, P. Perner and L. Shapiro (eds.), 2008.
Punko, Natalja, Rüping, Stefan and Wrobel, Stefan. Facilitating Clinico-Genomic Knowledge Discovery by Automatic Selection of KDD Processes. ICML 2008 Workshop on Machine Learning for Health Care Applications, Helsinki, Finland, July 9, 2008.
Wegener, Dennis and Sengstag, Thierry and Sfakianakis, Stelios and Rüping, Stefan. Supporting parallel R code in clinical trials: a grid-based approach. In Proc. of the 4th IEEE ISPA 2008 Workshop on High Performance and Grid Computing in Medicine and Biology (HiPGCoMB08), Sydney, Australia, December, 2008.
Tsiknakis, Manolis, Sfakianakis, Stelios, Rüping, Stefan, Trelles, Oswaldo, Sengstag, Thierry, Claerhout, Brecht, Virvilis, Vassilis: A Semantic Grid Services Architecture in Support of Efficient Knowledge Discovery from Multilevel Clinical and Genomic Datasets. In: Proceedings of the First International Conference on Health Informatics (HEALTHINF), Funchal, Madeira, Portugal, 279-287, 2008.
Paaß, Gerhard, Reinhardt, Wolf, Rüping, Stefan, Wrobel, Stefan. Data mining for security and crime detection. In: Security informatics and terrorism: social and technical problems of detecting and controlling terrorists' use of the World Wide Web ; proceedings of the NATO Advanced Research Workshop on Security Informatics and Terrorism - Patrolling the Web, Gal, Cecilia S. (Ed.). Beer-Sheva, Israel, 4-5 June 2007. Amsterdam [u.a.]: IOS Press, 2008. (NATO ASI series : Series D, Information and Communication Security 15), pp. 56-70
Rendenbach, Hans-Georg und Rüping, Stefan. Controlling in dynamischem Umfeld. Versicherungswirtschaft, 63. Jahrgang, Heft 11, 2008.
Krause-Traudes, Maike, Scheider, Simon, Rüping, Stefan, Meßner, Harald. Spatial data mining for retail sales forecasting. In: AGILE 2008 conference proceedings / CD-ROM: taking geoinformation science one step further, Bernard, Lars (Ed.): . AGILE, 2008.
Wegener, Dennis, and Sengstag, Thierry, and Sfakianakis, Stelios and Rüping, and Assi, Anthony. GridR: An R-based grid-enabled tool for data analysis in ACGT clinico-genomic trials. In: Proceedings of the 3rd International Conference on e-Science and Grid Computing (eScience 2007), Bangalore, India, 2007.
Rüping, Stefan, Sfakianakis, Stelios and Tsiknakis, Manolis. Extending Workflow Management for Knowledge Discovery in Clinico-Genomic Data. In: From Genes to Personalized HealthCare: Grid Solutions for the Life Sciences, Proceedings of HealthGrid 2007, pp. 183-193, IOS Press, 2007.
Potamias, George, Koumakis, Lefteris, Kanterakis, Alexandros, Sfakianakis, Stelios, Analyti, Anastasia, Moustakis, Vassilis, Kafetzopoulos, Dimitris, Rüping, Stefan, Tsiknakis, Manolis: Knowledge Discovery Scientific Workflows in Clinico-Genomics. 19th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2007), October 29-31, 2007, Patras, Greece, 91-95, 2007.
Rüping, Stefan. Learning Interpretable Models. Ph.D. Thesis, Universität Dortmund, 2006. URL: http://hdl.handle.net/2003/23008.
May, Michael and Potamias, George and Rüping, Stefan. Grid-based Knowledge Discovery in Clinico-Genomic Data. In: Proceedings of the 7th International Symposium on Biological and Medical Data Analysis (ISBMDA 2006), pp. 219-230, Springer, 2006.
Rüping, Stefan. Robust Probabilistic Calibration. In: Proceedings of the European Conference on Machine Learning (ECML), pp. 743-750, Springer, 2006.
Morik, Katharina and Rüping, Stefan. Klassifikations-/Clustermethoden und Konjunkturanalyse. Duncker & Humbolt, 2006.
Rüping, Stefan. Interpreting Classifiers by Multiple Views. In: Proceedings of the ICML 2005 Workshop on Learning with Multiple Views, 2005.
Rüping, Stefan. Classification with Local Models. In: Morik, K. and Boulicaut, J.-F. and Siebes, A. (editors), Proceedings of the Dagstuhl Workshop on Detecting Local Patterns, Springer, 2005.
Rüping, Stefan and Scheffer, Tobias (editors). Proceedings of the ICML 2005 Workshop on Learning with Multiple Views. 2005.
Pumplün, Constanze and Rüping, Stefan and Morik, Katharina and Weihs, Claus. D-Optimal plans in observational studies. Technical report 44/05, SFB475, University of Dortmund. 2005.
Christmann, Andreas and Luebke, Karsten and Rüping, Stefan and Marin-Galiano, Marcos. Determination of Hyper-Parameters for Kernel-based Classification and Regression. Technical report 38/05, SFB475, University of Dortmund. 2005.
Rüping, Stefan. A Simple Method for Estimating Conditional Probabilities in SVMs. In: Abecker, A. and Bickel, S. and Brefeld, U. and Drost, I. and Henze,N. and Herden, O. and Minor, M. and Scheffer, T. and Stojanovic, L. and Weibelzahl, S. (editors), LWA 2004 - Lernen - Wissensentdeckung - Adaptivität, Humboldt-Universität Berlin, 2004.
Klinkenberg, Ralf and Rüping, Stefan. Concept Drift and the Importance of Examples. In: Franke, Jürgen and Nakhaeizadeh, Gholamreza and Renz, Ingrid (editors), Text Mining - Theoretical Aspects and Applications, pp. 55-77, Berlin, Germany, Physica-Verlag, 2003.
Rüping, Stefan and Morik, Katharina. Support Vector Machines and Learning about Time. In: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'03), 2003.
Morik, Katharina and Joachims, Thorsten and Imhoff, Michael and Brockhausen, Peter and Rüping, Stefan. Integrating Kernel Methods into a Knowledge-based Approach to Evidence-based Medicine. In: Schmitt, M. and Teodorescu, H. and Jain, A. and Jain, A. and Jain, S. and Jain, L. (editors), Computational Intelligence Processing in Medical Diagnosis, Vol. 96, pp. 71-99, Physica-Verlag, 2002.
Morik, Katharina and Rüping, Stefan. A Multistrategy Approach to the Classification of Phases in Business Cycles. In: Elomaa, Taprio and Mannila, Heikki and Toivonen, Hannu (editors), Machine Learning: ECML 2002, Vol. 2430, pp. 307-318, Berlin, Springer, 2002.
Stefan Rüping. Support Vector Machines in Relational Databases. In: Seong-Whan Lee and Alessandro Verri (editors), Pattern Recognition with Support Vector Machines - First International Workshop, SVM 2002, pp. 310-320, Springer, 2002.
Rüping, Stefan. Efficient Kernel Calculation for Multirelational Data. In: Kokai, Gabriella and Zeidler, Jens (editors), Proceedings der FGML 2002, pp. 121-126, Learning Lab Lower Saxony, Hannover, Germany, 2002.
Klinkenberg, Ralf and Rüping, Stefan and Fick, Andreas and Henze, Nicola and Herzog, Christian and Molitor, Ralf and Schrüder, Olaf (editors). LLWA 01 - Tagungsband der GI-Workshop-Woche Lernen - Lehren - Wissen - Adaptivität. No. 763, Dortmund, Germany, 2001.
Rüping, Stefan. SVM Kernels for Time Series Analysis. In: Klinkenberg, Ralf and Rüping, Stefan and Fick, Andreas and Henze, Nicola and Herzog, Christian and Molitor, Ralf and Schrüder, Olaf (editors), LLWA 01 - Tagungsband der GI-Workshop-Woche Lernen - Lehren - Wissen - Adaptivität, pp. 43-50, Dortmund, Germany, 2001.
Rüping, Stefan. Incremental Learning with Support Vector Machines. In: Cercone, Nick and Lin, T.Y. and Wu, Xindong (editors), Proceedings of the 2001 IEEE International Conference on Data Mining (ICDM '01), pp. 641-642, IEEE, 2001.
Rüping, Stefan. Zeitreihenprognose für Warenwirtschaftssysteme unter Berücksichtigung asymmetrischer Kostenfunktionen. Universität Dortmund, 1999. In German.