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ORCIDHenrik Grosskreutz
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2010 – 2019
- 2013
[c22]Daniel Trabold, Henrik Grosskreutz:
Parallel subgroup discovery on computing clusters - First results. IEEE BigData 2013: 575-579- [c21]Björn Jacobs, Henrik Grosskreutz:
Towards Distributed Emerging Pattern Mining on Itemset Streams. LWA 2013: 124-127 - [c20]Henrik Grosskreutz, Bastian Lang, Daniel Trabold:
A Relevance Criterion for Sequential Patterns. ECML/PKDD (1) 2013: 369-384 - 2012
- [c19]Henrik Großkreutz, Daniel Paurat, Stefan Rüping:
An enhanced relevance criterion for more concise supervised pattern discovery. KDD 2012: 1442-1450 - [c18]Henrik Grosskreutz:
Class Relevant Pattern Mining in Output-Polynomial Time. SDM 2012: 284-294 - 2011
- [j11]Henrik Grosskreutz, Benedikt Lemmen, Stefan Rüping:
Secure Distributed Subgroup Discovery in Horizontally Partitioned Data. Trans. Data Priv. 4(3): 147-165 (2011) - [c17]Henrik Grosskreutz, Daniel Paurat:
Fast and Memory-Efficient Discovery of the Top-k Relevant Subgroups in a Reduced Candidate Space. ECML/PKDD (1) 2011: 533-548 - [i2]Michael Beetz, Henrik Grosskreutz:
Probabilistic Hybrid Action Models for Predicting Concurrent Percept-driven Robot Behavior. CoRR abs/1109.6030 (2011) - 2010
- [j10]Ton de Jong, Wouter R. van Joolingen, Adam Giemza, Isabelle Girault, Ulrich Hoppe, Jörg Kindermann, Anders Kluge, Ard W. Lazonder, Vibeke Vold, Armin Weinberger, Stefan Weinbrenner, Astrid Wichmann, Anjo Anjewierden, Marjolaine Bodin
, Lars Bollen, Cédric D'Ham, Jan Dolonen, Jan Engler, Caspar Geraedts, Henrik Grosskreutz, Tasos Hovardas, Rachel Julien, Judith Lechner, Sten R. Ludvigsen, Yuri Matteman, Øyvind Meistadt, Bjørge Næss, Muriel Ney, Margus Pedaste, Anthony Perritano, Marieke Rinket, Henrik Von Schlanbusch, Tago Sarapuu, Florian Schulz, Jakob Sikken, James D. Slotta, Jeremy Toussaint, Alex Verkade, Claire Wajeman, Barbara Wasson, Zacharias C. Zacharia, Martine Van Der Zanden:
Learning by creating and exchanging objects: The SCY experience. Br. J. Educ. Technol. 41(6): 909-921 (2010) - [j9]Henrik Grosskreutz, Benedikt Lemmen, Stefan Rüping:
Privacy-Preserving Data-Mining. Inform. Spektrum 33(4): 380-383 (2010) - [c16]Henrik Grosskreutz, Mario Boley, Maike Krause-Traudes:
Subgroup Discovery for Election Analysis: A Case Study in Descriptive Data Mining. Discovery Science 2010: 57-71 - [c15]Henrik Grosskreutz, Benedikt Lemmen, Stefan Rüping:
Secure Top-k Subgroup Discovery. PSDML 2010: 36-49 - [c14]Mario Boley, Thomas Gärtner, Henrik Grosskreutz:
Formal Concept Sampling for Counting and Threshold-Free Local Pattern Mining. SDM 2010: 177-188
2000 – 2009
- 2009
- [j8]Henrik Grosskreutz, Stefan Rüping:
On subgroup discovery in numerical domains. Data Min. Knowl. Discov. 19(2): 210-226 (2009) - [j7]Mario Boley, Henrik Grosskreutz:
Approximating the number of frequent sets in dense data. Knowl. Inf. Syst. 21(1): 65-89 (2009) - [c13]Henrik Grosskreutz, Stefan Rüping:
On Subgroup Discovery in Numerical Domains. ECML/PKDD (1) 2009: 30 - [c12]Mario Boley, Henrik Grosskreutz:
Non-redundant Subgroup Discovery Using a Closure System. ECML/PKDD (1) 2009: 179-194 - 2008
- [j6]Stefan Rüping, Natalja Punko, Björn Günter, Henrik Grosskreutz:
Procurement Fraud Discovery using Similarity Measure Learning. Trans. Case Based Reason. 1(1): 37-46 (2008) - [c11]Mario Boley, Henrik Grosskreutz:
A Randomized Approach for Approximating the Number of Frequent Sets. ICDM 2008: 43-52 - [c10]Stefan Rüping, Natalja Punko, Björn Günter, Henrik Grosskreutz:
Procurement Fraud Discovery using Similarity Measure Learning. ICDM (Posters and Workshops) 2008: 55-64 - [c9]Henrik Grosskreutz, Stefan Rüping, Stefan Wrobel:
Tight Optimistic Estimates for Fast Subgroup Discovery. ECML/PKDD (1) 2008: 440-456 - 2005
- [j5]Michael Beetz, Henrik Grosskreutz:
Probabilistic Hybrid Action Models for Predicting Concurrent Percept-driven Robot Behavior. J. Artif. Intell. Res. 24: 799-849 (2005) - 2003
- [j4]Henrik Grosskreutz, Gerhard Lakemeyer:
Probabilistic Complex Actions in GOLOG. Fundam. Informaticae 57(2-4): 167-192 (2003) - [j3]Henrik Grosskreutz, Gerhard Lakemeyer:
ccGolog -- A Logical Language Dealing with Continuous Change. Log. J. IGPL 11(2): 179-221 (2003) - 2001
- [j2]Michael Beetz, Tom Arbuckle, Thorsten Belker, Armin B. Cremers, Dirk Schulz, Maren Bennewitz, Wolfram Burgard, Dirk Hähnel, Dieter Fox, Henrik Grosskreutz:
Integrated Plan-Based Control of Autonomous Robots in Human Environments. IEEE Intell. Syst. 16(5): 56-65 (2001) - [c8]Henrik Grosskreutz, Gerhard Lakemeyer:
On-Line Execution of cc-Golog Plans. IJCAI 2001: 12-18 - [c7]Henrik Grosskreutz, Gerhard Lakemeyer:
Belief Update in the pGOLOG Framework. KI/ÖGAI 2001: 213-228 - 2000
- [j1]Henrik Grosskreutz, Gerhard Lakemeyer:
Towards more Realistic Logic-based Robot Controllers in the GOLOG Framework. Künstliche Intell. 14(4): 11-15 (2000) - [c6]Henrik Grosskreutz, Gerhard Lakemeyer:
cc-Golog: Towards More Realistic Logic-Based Robot Controllers. AAAI/IAAI 2000: 476-482 - [c5]Michael Beetz, Henrik Grosskreutz:
Probabilistic Hybrid Action Models for Predicting Concurrent Percept-Driven Robot Behavior. AIPS 2000: 42-51 - [c4]Henrik Grosskreutz, Gerhard Lakemeyer:
Turning High-Level Plans into Robot Programs in Uncertain Domains. ECAI 2000: 548-552 - [c3]Henrik Grosskreutz:
Probabilistic Projection and Belief Update in the pGOLOG Framework. GI Jahrestagung 2000: 233-249 - [i1]Henrik Grosskreutz, Gerhard Lakemeyer:
cc-Golog: Towards More Realistic Logic-Based Robot Controllers. CoRR cs.AI/0003032 (2000)
1990 – 1999
- 1999
- [c2]Michael Beetz, Maren Bennewitz, Henrik Grosskreutz:
Probabilistic, Prediction-Based Schedule Debugging for Autonomous Robot Office Couriers. KI 1999: 243-254 - 1998
- [c1]Michael Beetz, Henrik Grosskreutz:
Causal Models of Mobile Service Robot Behavior. AIPS 1998: 163-170
Coauthor Index
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