Research

Conference and Workshop Papers

• F. Höppner, F. Klawonn: Compensation of Translational Displacement in Time Series Clustering Using Cross Correlation In: Proc. 8th Int. Symp. Intelligent Data Analyis (IDA) (2009), 71-82, © Springer
• F. Höppner: How much true structure has been discovered? -- Validating Explorative Clustering on a Hold-Out Test Set In: Proc. Machine Learning and Data Mining in Pattern Recognition (MLDM) (2009), 385--397, © Springer
• F. Höppner, A. Topp: Classification Based on the Trace of Variables over Time. Proc. Int. Conf. Intelligent Data Engineering and Automated Learning (IDEAL) (2007), 739--749 © Springer
• F. Höppner, M. Böttcher: Matching Partitions over Time to Reliably Capture Local Clusters in Noisy Domains. Proc. Int. Conf. Principles and Practice of Knowledge Discovery in Databases PKDD (2007), 479-486 [ .pdf ] © Springer
• K. Tschumitschew, F. Klawonn, F. Höppner, V. Kolodyazhniy: Landscape Multidimensional Scaling. Proc. Symp. on Intelligent Data Analysis (IDA) (2007), 263-273, © Springer.
• F. Höppner: Klassifikation von Zeitreihen und Sequenzen. Proc. 16th Workshop Computational Intelligence (2006), 179-189
• F. Höppner, F. Klawonn: Visualising Clusters in High-Dimensional Data Sets by Intersecting Spheres. Proc. International Symposium on Evolving Fuzzy Systems (2006), 106-111 [ .pdf ]
• F. Klawonn, F. Höppner: Equi-sized, Homogeneous Partitioning. Int. Conf. on Knowledge-Based Intelligent Engineering Systems and Allied Technologies, Springer LNCS 4252 (2006), 70-77 [ .pdf ] © Springer
• F. Höppner: Objective Function-Based Discretization. Proc. 29th Annual Conf. of the Gesellschaft für Klassifikation, Springer, Berlin (2005): 483-445
• F. Höppner: Local Pattern Detection and Clustering -- Are there substantive differences? In: K. Morik, J.-F. Boulicaut, A. Siebes (eds.): Local Pattern Detection © Springer, Berlin (2005): 53-70 [ .pdf ] © Springer
• F. Klawonn, F. Höppner: An Alternative Approach to the Fuzzifier in Fuzzy Clustering to Obtain Better Clustering Results. In: Proceedings 3rd Eusflat. EUSFLAT, Zittau (2003), 730-734 [ .pdf ]
• F. Klawonn, F. Höppner: What is Fuzzy About Fuzzy Clustering? -- Understanding and Improving the Concept of the Fuzzifier. In: M.R. Berthold, H.-J. Lenz, E. Bradley, R. Kruse, C. Borgelt (eds.): Advances in Intelligent Data Analysis V. © Springer, Berlin (2003), 254-264 [ .pdf ] © Springer
• F. Höppner: Handling feature ambiguity in knowledge discovery from time series. In Proc. of the Int. Conf. on Discovery Science, LNCS 2534, pp. 398-405. Lübeck, Germany, 2002. [ .ps.gz ] [ .pdf ] © Springer
• F. Höppner: Time series abstraction methods -- A Survey. Tagungsband zur 32. GI Jahrestagung 2002, Workshop on Knowledge Discovery in Databases, Dortmund, pp. 777-786, Sept/Okt. 2002. [ .ps.gz ] [ .pdf ]
• F. Höppner: Discovery of core episodes from sequences -- using generalization for defragmentation of rule sets. In Pattern Detection and Discovery in Data Mining, LNAI 2447, pp. 199-213. London, England, Sept 2002. [ .ps.gz ] [ .pdf ] © Springer
• F. Höppner: Learning Dependencies in Multivariate Time Series. Proc. of the ECAI'02 Workshop on Knowledge Discovery in (Spatio-) Temporal Data, Lyon, France, pp. 25-31, July 2002. [ .ps.gz ] [ .pdf ]
• F. Höppner: Lernen lokaler Zusammenhänge in multivariaten Zeitreihen. Tagungsband zum 5. Göttinger Symposium Soft Computing, Göttingen, pp. 113-125, Juni 2002. [ .ps.gz ] [ .pdf ]
• M. Ortolani, H. Hofer, D. Patterson, F. Höppner and M. Berthold: Fuzzy Information Granules in Time Series Data. Proc. of IEEE Int. Conf. on Fuzzy Systems, Honolulu, Hawai, pp. 695-699, May 2002.
• F. Höppner, F. Klawonn: Finding Informative Rules in Interval Sequences. Advances in Intelligent Data Analysis, Proc. of the 4th International Symposium, Lecture Notes in Computer Sciences 2189, Springer. Lissabon, Portugal, pp. 123-132, Sept. 2001. [ .ps.gz ] [ .pdf ] © Springer
• F. Höppner: Discovery of Temporal Patterns - Learning Rules about the Qualitative Behaviour of Time Series. Proc. of the 5th European Conference on Principles and Practice of Knowledge Discovery in Databases, Lecture Notes in Artificial Intelligence 2168, Springer. Freiburg, Germany, pp. 192-203, Sept. 2001. [ .ps.gz ] [ .pdf ] © Springer
• F. Höppner: Learning Temporal Rules from State Sequences. IJCAI Workshop on Learning from Temporal and Spatial Data, Seattle, USA, pp. 25-31, 2001. [ .ps.gz ] [ .pdf ]
• F. Höppner, F. Klawonn: A New Approach to Fuzzy Partitioning. Proc. of the Joint 9th IFSA World Congress and 20th NAFIPS International Conference, Vancouver, Canada, pp. 1419-1424, 2001. [ .ps.gz ] [ .pdf ]
• F. Höppner, F. Klawonn: Obtaining Interpretable Fuzzy Models from Fuzzy Clustering and Fuzzy Regression. Proc. of the 4th Int. Conf. on Knowledge-Based Intelligent Engineering Systems and Allied Technologies (KES), Brighton, UK, pp. 162-165, 2000. [ .ps.gz ] [ .pdf ]
• F. Höppner, F. Klawonn: Fuzzy Clustering of Sampled Functions. Proc. of the 19th Int. Conf. of the North American Fuzzy Information Processing Society (NAFIPS), Atlanta, USA, pp. 251-255, 2000. [ .ps.gz ] [ .pdf ]
• F. Höppner: Piecewise Linear Function Approximation by Alternating Optimization. Proc. of the 8th Int. Conf. on Information Processing and Management of Uncertainty in Knowledge Based Systems (IPMU), Madrid, Spain, pp. 1751-1757, 2000.