Information Engineering Lab
F. Höppner, Ostfalia University of Applied Sciences
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Areas of Interest
My areas of interest include cluster
analysis and temporal pattern
discovery (e.g. in time series). The SimTrade project has been designed to arouse
the students curiosity in data analysis, by letting students of higher
semesters analyse the data generated by first-year students.
Data Sets
Data sets for
linear transformation distance,
adaptive filter distance, and for
ROSMULD clustering are available.
Publications
(Alternative list on the DBLP computer science bibliography).
Books / Proceedings
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M.R. Berthold, C. Borgelt, F. Höppner, F. Klawonn, R. Silipo: Guide to Intelligent Data Science (2nd edition), Springer 2020.
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A. Tucker, F. Höppner, A. Siebes, S. Swift: Advances in Intelligent Data Analysis XII., Springer 2013.
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H. Helmke, F. Höppner, R. Isernhagen:
Einführung in die Softwareentwicklung.
Hanser, München, 2007.
- F. Höppner, F. Klawonn, R. Kruse, T. Runkler:
Fuzzy Cluster Analysis.
Wiley, Chichester, 1999.
- F. Höppner, F. Klawonn, R. Kruse:
Fuzzy-Clusteranalyse:
Verfahren für die Bilderkennung, Klassifikation und Datenanalyse.
Reihe Computational Intelligence, Vieweg, Braunschweig, 1997.
Journal Papers and Book Chapters
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F. Höppner: Improving time series similarity measures by integrating preprocessing steps. Data Min. Knowl. Discov. 31(3): 851-878, 2017
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F. Höppner, S. Peter: Temporal interval pattern languages to characterize time flow. Wiley Interdisc. Rew.: Data Mining and Knowledge Discovery 4(3): 196-212 (2014)
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B. Wiswedel, F. Höppner, M. R. Berthold: Learning in parallel universes. Data Min. Knowl. Discov. 21(1): 130-152 (2010)
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M. Böttcher, F. Höppner, M. Spiliopoulou:
On exploiting the power of time in data mining
SIGKDD Explorations 10(2): 3-11 (2008)
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F. Höppner, F. Klawonn:
Clustering with Size Constraints
In: Computational Intelligence Paradigms, Springer, 2008.
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F. Höppner:
Association Rules
In: O. Maimon, L. Rokach (eds.):
The Data Mining and Knowledge Discovery Handbook.
© Springer, Berlin (2005): 353-376
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F. Höppner, F. Klawonn:
Learning Fuzzy Systems -- An Objective-Function Approach.
Mathware and Soft Computing Journal, 11(5) 2004, 143--162
- F. Höppner, F. Klawonn:
A Contribution to Convergence Theory of Fuzzy c-Means and Derivatives.
IEEE Trans. on Fuzzy Systems, 11(5), pp. 682-694, 2003.
- F. Höppner, F. Klawonn:
Improved Fuzzy Partitions for Fuzzy Regression Models.
International Journal of Approximate Reasoning (32), 85-102, 2003.
- F. Höppner, F. Klawonn:
Finding Informative Rules in Interval Sequences.
Intelligent Data Analysis - An International Journal, 6(3), 237-256, 2002.
- F. Höppner, F. Klawonn:
Learning Rules about the Development of Variables over Time.
In: C.T. Leondes (editor): Intelligent Systems -
Techniques and Applications, vol IV, CRC Press, 201-228, 2002.
- F. Höppner:
Speeding up Fuzzy c-Means: Using a Hierarchical Data Organisation to Control the Precision of Membership Calculation.
Fuzzy Sets and Systems, 128(3), pp. 365-378, 2002.
- F. Höppner, F. Klawonn, P. Eklund:
Learning Indistinguishability from Data.
Soft Computing Journal 6(1), pp. 6-13, 2002
- F. Höppner:
Fuzzy Shell Clustering Algorithms in Image Processing: Fuzzy c-Rectangular and 2-Rectangular Shells.
IEEE Transactions on Fuzzy Systems, 5(4), pp. 599-613, 1997.
Conference and Workshop Papers
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T. Haumann, F. Höppner:
Adaptivity of Card Recommendation Systems for Legends of Code and Magic.
IEEE Conference on Games (COG), Milan, Italy, 2024
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L. E. Brauner, F. Höppner:
Enhancing Computer Science Education by Automated Analysis of Students’ Code Submissions.
Ai4Ai Workshop at European Conf. on Artificial Intelligence (ECAI), Springer CCIS series vol 1948 (2023), 369-380
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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)
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F. Höppner: Grouping Source Code by Solution Approaches - Improving Feedback in Programming Courses, Educational Data Mining (EDM) 2021
[ .pdf ]
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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)
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F. Höppner, M. Jahnke: Enriched Weisfeiler-Lehman Kernel for Improved Graph Clustering of Source Code, IDA 2020, 248-260
[ .pdf ] (open access)
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F. Höppner: Multidimensional Decision Tree Splits to Improve Interpretability, KES 2020, 156-165
[ .pdf ] (open access)
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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 ]
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M. Wohlan, Y. Schröder, F. Höppner: Generating ``Who Wants to Be a Millionaire?'' Questions Sets Automatically from Wikidata, Poster Track at SEMANTiCS 2019
[ .pdf ]
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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 ]
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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 ]
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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 ]
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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 ]
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T. Sobek, F. Höppner: Visual Perception of Discriminative Landmarks in Classified Time Series. Proc. Int. Symp. Intelligent Data Analysis, 2016, pp. 73-85
[ .pdf ]
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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 ]
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F. Höppner: Optimal Filtering for Time Series Classification. IDEAL '15 Proc. 16th Int. Conf. on Intelligent Data Engineering and Automated Learning, Wroclaw, Poland.
[ .pdf ]
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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 ]
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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.
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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 ]
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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.
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F. Höppner, S. Peter, M. R. Berthold: Enriching Multivariate Temporal Patterns with Context Information to Support Classification. Computational Intelligence in IDA, 2013.
[ .pdf ]
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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
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S. Peter, F. Höppner, M. R. Berthold: Learning pattern graphs for multivariate temporal pattern retrieval. IDA 2012, 264-275.
[ .pdf ]
© Springer
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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 ].
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F. Klawonn, F. Höppner, S. May: An Alternative to ROC and AUC Analysis of Classifiers. IDA 2011, 210-221
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S. Peter, F. Höppner: Finding Temporal Patterns Using Constraints on (Partial) Absence, Presence and Duration. KES (1) 2010: 442-451
[ .pdf ]
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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
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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
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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
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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
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K. Tschumitschew, F. Klawonn, F. Höppner, V. Kolodyazhniy:
Landscape Multidimensional Scaling.
Proc. Symp. on Intelligent Data Analysis (IDA) (2007), 263-273,
© Springer.
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F. Höppner:
Klassifikation von Zeitreihen und Sequenzen.
Proc. 16th Workshop Computational Intelligence (2006), 179-189
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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 ]
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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
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F. Höppner:
Objective Function-Based Discretization.
Proc. 29th Annual Conf. of the Gesellschaft für Klassifikation,
Springer, Berlin (2005): 483-445
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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
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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 ]
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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.