Information Engineering Lab

Conference and Workshop Papers

• M. Jahnke, F. Höppner: Is there Method in Your Mistakes? Capturing Error Contexts by Graph Mining for Targeted Feedback, Educational Data Mining (EDM) 2022 [ .pdf ] (open access)
• F. Höppner: Grouping Source Code by Solution Approaches - Improving Feedback in Programming Courses, Educational Data Mining (EDM) 2021 [ .pdf ]
• F. Höppner: Taking benefit from fellow students code without copying off -- making better use of students collective work, DeLFI 2020, 217-222 [ .pdf ] (open access)
• F. Höppner, M. Jahnke: Enriched Weisfeiler-Lehman Kernel for Improved Graph Clustering of Source Code, IDA 2020, 248-260 [ .pdf ] (open access)
• F. Höppner: Multidimensional Decision Tree Splits to Improve Interpretability, KES 2020, 156-165 [ .pdf ] (open access)
• F. Höppner, M. Jahnke: Holistic Assessment of Structure Discovery Capabilities of Clustering Algorithm, Proc. Int. Conf. Principles and Practices of Knowledge Discovery in Databases (PKDD) 2019 [ .pdf ]
• M. Wohlan, Y. Schröder, F. Höppner: Generating ``Who Wants to Be a Millionaire?'' Questions Sets Automatically from Wikidata, Poster Track at SEMANTiCS 2019 [ .pdf ]
• F. Höppner: Measuring Instruction Comprehension by Mining Memory Traces for Early Formative Feedback in Java Courses, Proc. Innovation and Technology in Computer Science Education (ITiCSE) 2019, 105-111 [ .pdf ]
• F. Höppner, J.-H. Hemmje: Zur automatischen Erkennung von Fehlkonzepten bei Java-Einsteigern durch Analyse von Speicher-Protokollen, Proc. 16. E-Learning Fachtagung Informatik. Bonn: Gesellschaft für Informatik e.V. (S. 165-170), Frankfurt, Germany, 2018 [ .pdf ]
• F. Höppner, T. Sobek: A Multiscale Bezier-Representation for Time Series that Supports Elastic Matching, Proc. Int. Conf. Principles and Practices of Knowledge Discovery in Databases (PKDD) 2017 [ .pdf ]
• R. Goltermann, F. Höppner: Internalizing a Viable Mental Model of Program Execution in First Year Programming Courses, Proc. 3rd Workshop 'Automatische Bewertung von Programmieraufgaben', Potsdam, Germany, 2017 [ .pdf ]
• T. Sobek, F. Höppner: Visual Perception of Discriminative Landmarks in Classified Time Series. Proc. Int. Symp. Intelligent Data Analysis, 2016, pp. 73-85 [ .pdf ]
• B. Schulten, F. Höppner: Zur Einschätzung von Programmierfähigkeiten - Jedem Programmieranfänger über die Schultern schauen. Proc. 2nd Workshop 'Automatische Bewertung von Programmieraufgaben', Wolfenbüttel, Germany, 2015. [ .pdf ]
• F. Höppner: Optimal Filtering for Time Series Classification. IDEAL '15 Proc. 16th Int. Conf. on Intelligent Data Engineering and Automated Learning, Wroclaw, Poland. [ .pdf ]
• A. Schweier, F. Höppner: Finding the Intrinsic Patterns in a Collection of Time Series. IDA '14 Proc. 13th Int. Symp. on Intelligent Data Analysis, Leuven, Belgium. [ .pdf ]
• F. Höppner: A subspace filter supporting the discovery of small clusters in very noisy datasets. SSDBM '14 Proc. 26th Int. Conf. on Scientific and Statistical Database Management: Article No. 14. Aalborg, Danmark. [ .pdf ] Best Paper Award.
• F. Höppner: Less is More: Similarity of Time Series under Linear Transformations. Proc. 2014 SIAM International Conference on Data Mining (SDM): 560-568. Philadelphia, USA. [ .pdf ]
• S. Peter, F. Höppner, M. R. Berthold: Pattern Graphs: Combining Multivariate Time Series and Labelled Interval Sequences for Classification.. 33rd SGAI Int. Conf. on AI 2013, 5-18 Best refereed technical paper at AI-2013.
• F. Höppner, S. Peter, M. R. Berthold: Enriching Multivariate Temporal Patterns with Context Information to Support Classification. Computational Intelligence in IDA, 2013. [ .pdf ]
• F. Klawonn, F. Höppner, B. Jayaram: What are Clusters in High Dimensions and are they Difficult to Find?. Proc. 1st Workshop 'Clustering High-Dimensional Data', 2012, pp 14-33
• S. Peter, F. Höppner, M. R. Berthold: Learning pattern graphs for multivariate temporal pattern retrieval. IDA 2012, 264-275. [ .pdf ] © Springer
• S. Peter, F. Höppner, M. R. Berthold: Pattern graphs: A knowledge-based tool for multivariate temporal pattern retrieval. IEEE Conf. Intelligent Systems, 2012. Selected among Best Papers of IS 2012 [ .pdf ].
• F. Klawonn, F. Höppner, S. May: An Alternative to ROC and AUC Analysis of Classifiers. IDA 2011, 210-221
• S. Peter, F. Höppner: Finding Temporal Patterns Using Constraints on (Partial) Absence, Presence and Duration. KES (1) 2010: 442-451 [ .pdf ]
• 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 [ .pdf ] © 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 [ .pdf ] © 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 [ .pdf ] © 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.