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Frank Höppner
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- affiliation: Ostfalia University of Applied Sciences, Department of Computer Science, Wolfenbüttel, Germany
- affiliation (PhD 2003): Technical University of Braunschweig, Braunschweig, Germany
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2020 – today
- 2024
- [c44]Timo Haumann, Frank Höppner:
Adaptivity of Card Recommendation Systems for Legends of Code and Magic. CoG 2024: 1-8 - [c43]Fabian Pfütsch, Frank Höppner:
Estimating and Differentiating Programming Skills from Unstructured Coding Exercises via Matrix Factorization. Koli Calling 2024: 7:1-7:9 - 2023
- [c42]Lea Eileen Brauner
, Frank Höppner
Enhancing Computer Science Education by Automated Analysis of Students' Code Submissions. ECAI Workshops (2) 2023: 369-380 - 2022
- [c41]Maximilian Jahnke, Frank Höppner:
Is there Method in Your Mistakes? Capturing Error Contexts by Graph Mining for Targeted Feedback. EDM 2022 - 2021
- [c40]Frank Höppner:
Grouping Source Code by Solution Approaches - Improving Feedback in Programming Courses. EDM 2021 - 2020
- [b4]Michael R. Berthold
Guide to Intelligent Data Science - How to Intelligently Make Use of Real Data, Second Edition. Texts in Computer Science, Springer 2020, ISBN 978-3-030-45573-6, pp. 1-328 - [c39]Frank Höppner:
Taking benefit from fellow students code without copying off - making better use of students collective work. DeLFI 2020: 217-222 - [c38]Frank Höppner
Enriched Weisfeiler-Lehman Kernel for Improved Graph Clustering of Source Code. IDA 2020: 248-260 - [c37]Frank Höppner:
Multidimensional Decision Tree Splits to Improve Interpretability. KES 2020: 156-165
2010 – 2019
- 2019
- [c36]Markus Wohlan, Yannik Schröder, Frank Höppner:
Generating "Who Wants to Be a Millionaire?" Questions Sets Automatically from Wikidata. SEMANTiCS (Posters & Demos) 2019 - [c35]Frank Höppner:
Measuring Instruction Comprehension by Mining Memory Traces for Early Formative Feedback in Java Courses. ITiCSE 2019: 105-111 - [c34]Frank Höppner, Maximilian Jahnke:
Holistic Assessment of Structure Discovery Capabilities of Clustering Algorithms. ECML/PKDD (1) 2019: 223-239 - 2018
- [c33]Frank Höppner, Jan-Hendrik Hemmje:
Zur automatischen Erkennung von Fehlkonzepten bei Java-Einsteigern durch Analyse von Speicher-Protokollen. DeLFI 2018: 165-170 - 2017
- [j11]Frank Höppner
Improving time series similarity measures by integrating preprocessing steps. Data Min. Knowl. Discov. 31(3): 851-878 (2017) - [c32]Robin Goltermann, Frank Höppner:
Internalizing a Viable Mental Model of Program Execution in First Year Programming Courses. ABP 2017 - [c31]Frank Höppner, Tobias Sobek:
A Multiscale Bezier-Representation for Time Series that Supports Elastic Matching. ECML/PKDD (2) 2017: 461-477 - 2016
- [c30]Tobias Sobek, Frank Höppner:
Visual Perception of Discriminative Landmarks in Classified Time Series. IDA 2016: 73-85 - [i2]Michael R. Berthold, Frank Höppner:
On Clustering Time Series Using Euclidean Distance and Pearson Correlation. CoRR abs/1601.02213 (2016) - 2015
- [c29]Bastian Schulten, Frank Höppner:
Zur Einschätzung von Programmierfähigkeiten - Jedem Programmieranfänger über die Schultern schauen. ABP 2015 - [c28]Frank Höppner:
Optimal Filtering for Time Series Classification. IDEAL 2015: 26-35 - 2014
- [j10]Frank Höppner, Sebastian Peter:
Temporal interval pattern languages to characterize time flow. WIREs Data Mining Knowl. Discov. 4(3): 196-212 (2014) - [c27]Anke Schweier, Frank Höppner:
Finding the Intrinsic Patterns in a Collection of Time Series. IDA 2014: 286-297 - [c26]Frank Höppner:
Efficient Identification of Subspaces with Small but Substantive Clusters in Noisy Datasets. LWA 2014: 107-108 - [c25]Frank Höppner:
Less is More: Similarity of Time Series under Linear Transformations. SDM 2014: 560-568 - [c24]Frank Höppner:
A subspace filter supporting the discovery of small clusters in very noisy datasets. SSDBM 2014: 14:1-14:12 - 2013
- [c23]Sebastian Peter, Frank Höppner, Michael R. Berthold:
Pattern Graphs: Combining Multivariate Time Series and Labelled Interval Sequences for Classification. SGAI Conf. 2013: 5-18 - [e1]Allan Tucker, Frank Höppner, Arno Siebes, Stephen Swift
Advances in Intelligent Data Analysis XII - 12th International Symposium, IDA 2013, London, UK, October 17-19, 2013. Proceedings. Lecture Notes in Computer Science 8207, Springer 2013, ISBN 978-3-642-41397-1 [contents] - 2012
- [c22]Frank Klawonn
What are Clusters in High Dimensions and are they Difficult to Find? CHDD 2012: 14-33 - [c21]Sebastian Peter, Frank Höppner, Michael R. Berthold:
Learning Pattern Graphs for Multivariate Temporal Pattern Retrieval. IDA 2012: 264-275 - [c20]Sebastian Peter, Frank Höppner, Michael R. Berthold:
Pattern graphs: A knowledge-based tool for multivariate temporal pattern retrieval. IEEE Conf. of Intelligent Systems 2012: 67-73 - 2011
- [c19]Frank Klawonn
An Alternative to ROC and AUC Analysis of Classifiers. IDA 2011: 210-221 - 2010
- [b3]Michael R. Berthold, Christian Borgelt, Frank Höppner, Frank Klawonn
Guide to Intelligent Data Analysis - How to Intelligently Make Sense of Real Data. Texts in Computer Science 42, Springer 2010, ISBN 978-1-84882-259-7, pp. I-XIII, 1-394 - [j9]Bernd Wiswedel, Frank Höppner, Michael R. Berthold:
Learning in parallel universes. Data Min. Knowl. Discov. 21(1): 130-152 (2010) - [c18]Sebastian Peter, Frank Höppner:
Finding Temporal Patterns Using Constraints on (Partial) Absence, Presence and Duration. KES (1) 2010: 442-451 - [p4]Frank Höppner:
Association Rules. Data Mining and Knowledge Discovery Handbook 2010: 299-319
2000 – 2009
- 2009
- [c17]Frank Höppner, Frank Klawonn
Compensation of Translational Displacement in Time Series Clustering Using Cross Correlation. IDA 2009: 71-82 - [c16]Frank Höppner:
How Much True Structure Has Been Discovered? MLDM 2009: 385-397 - [p3]Roland Winkler, Frank Klawonn
Fuzzy Cluster Analysis of Larger Data Sets. Scalable Fuzzy Algorithms for Data Management and Analysis 2009: 302-331 - 2008
- [j8]Mirko Böttcher, Frank Höppner, Myra Spiliopoulou:
On exploiting the power of time in data mining. SIGKDD Explor. 10(2): 3-11 (2008) - [p2]Frank Höppner, Frank Klawonn
Clustering with Size Constraints. Computational Intelligence Paradigms 2008: 167-180 - 2007
- [b2]Hartmut Helmke, Frank Höppner, Rolf Isernhagen:
Einführung in die Softwareentwicklung - vom Programmieren zur erfolgreichen Software-Projektarbeit: am Beispiel von Java und C++. Hanser 2007, pp. I-XVI, 1-383 - [c15]Katharina Tschumitschew, Frank Klawonn, Frank Höppner, Vitaliy Kolodyazhniy:
Landscape Multidimensional Scaling. IDA 2007: 263-273 - [c14]Frank Höppner, Alexander Topp:
Classification Based on the Trace of Variables over Time. IDEAL 2007: 739-749 - [c13]Frank Höppner, Mirko Böttcher:
Matching Partitions over Time to Reliably Capture Local Clusters in Noisy Domains. PKDD 2007: 479-486 - [i1]Frank Höppner, Mirko Böttcher:
Reliably Capture Local Clusters in Noisy Domains From Parallel Universes. Parallel Universes and Local Patterns 2007 - 2006
- [c12]Frank Klawonn, Frank Höppner:
Equi-sized, Homogeneous Partitioning. KES (2) 2006: 70-77 - 2005
- [c11]Frank Höppner:
Objective Function-based Discretization. GfKl 2005: 438-445 - [p1]Frank Höppner:
Association Rules. The Data Mining and Knowledge Discovery Handbook 2005: 353-376 - 2004
- [j7]Michael R. Berthold, Marco Ortolani
Fuzzy information granules in time series data. Int. J. Intell. Syst. 19(7): 607-618 (2004) - [c10]Frank Höppner:
Local Pattern Detection and Clustering. Local Pattern Detection 2004: 53-70 - 2003
- [b1]Frank Höppner:
Knowledge discovery from sequential data. Braunschweig University of Technology, Germany, 2003, pp. 1-163 - [j6]Frank Höppner, Frank Klawonn
Improved fuzzy partitions for fuzzy regression models. Int. J. Approx. Reason. 32(2-3): 85-102 (2003) - [j5]Frank Höppner, Frank Klawonn
A contribution to convergence theory of fuzzy c-means and derivatives. IEEE Trans. Fuzzy Syst. 11(5): 682-694 (2003) - [c9]Frank Klawonn, Frank Höppner:
An alternative approach to the fuzzifier in fuzzy clustering to obtain better clustering. EUSFLAT Conf. 2003: 730-734 - [c8]Frank Klawonn, Frank Höppner:
What Is Fuzzy about Fuzzy Clustering? Understanding and Improving the Concept of the Fuzzifier. IDA 2003: 254-264 - 2002
- [j4]Frank Höppner:
Speeding up fuzzy c-means: using a hierarchical data organisation to control the precision of membership calculation. Fuzzy Sets Syst. 128(3): 365-376 (2002) - [j3]Frank Höppner, Frank Klawonn:
Finding informative rules in interval sequences. Intell. Data Anal. 6(3): 237-255 (2002) - [j2]Frank Höppner, Frank Klawonn, Patrik Eklund:
Learning indistinguishability from data. Soft Comput. 6(1): 6-13 (2002) - [c7]Frank Höppner:
Handling Feature Ambiguity in Knowledge Discovery from Time Series. Discovery Science 2002: 398-405 - [c6]Frank Höppner:
Discovery of Core Episodes from Sequences. Pattern Detection and Discovery 2002: 199-213 - [c5]Marco Ortolani, Heiko Hofer, David E. Patterson, Frank Höppner, Michael R. Berthold:
Fuzzy information granules in time series data. FUZZ-IEEE 2002: 695-699 - [c4]Frank Höppner:
Time Series Abstraction Methods - A Survey. GI Jahrestagung 2002: 777-786 - 2001
- [c3]Frank Höppner, Frank Klawonn
Finding Informative Rules in Interval Sequences. IDA 2001: 125-134 - [c2]Frank Höppner:
Discovery of Temporal Patterns. Learning Rules about the Qualitative Behaviour of Time Series. PKDD 2001: 192-203 - 2000
- [c1]Frank Höppner, Frank Klawonn:
Obtaining interpretable fuzzy models from fuzzy clustering and fuzzy regression. KES 2000: 162-165
1990 – 1999
- 1997
- [j1]Frank Hoeppner:
Fuzzy shell clustering algorithms in image processing: fuzzy C-rectangular and 2-rectangular shells. IEEE Trans. Fuzzy Syst. 5(4): 599-613 (1997)
Coauthor Index
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last updated on 2024-11-19 20:48 CET by the dblp team
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