| A relevance criterion for sequential patterns H Grosskreutz, B Lang, D Trabold Machine Learning and Knowledge Discovery in Databases: European Conference …, 2013 | 18 | 2013 |
| Multilingual knowledge-based concept recognition in textual data M Schierle, D Trabold Advances in Data Analysis, Data Handling and Business Intelligence …, 2010 | 18 | 2010 |
| Comparison of structured vs. unstructured data for industrial quality analysis C Hänig, M Schierle, D Trabold Proceedings of the World Congress on Engineering and Computer Science 1, 20-22, 2010 | 14 | 2010 |
| Simple recurrent neural networks for support vector machine training R Sifa, D Paurat, D Trabold, C Bauckhage Artificial Neural Networks and Machine Learning–ICANN 2018: 27th …, 2018 | 11 | 2018 |
| Mining strongly closed itemsets from data streams D Trabold, T Horváth Discovery Science: 20th International Conference, DS 2017, Kyoto, Japan …, 2017 | 11 | 2017 |
| Extraction of failure graphs from structured and unstructured data M Schierle, D Trabold 2008 Seventh International Conference on Machine Learning and Applications …, 2008 | 9 | 2008 |
| Parallel subgroup discovery on computing clusters—First results D Trabold, H Grosskreutz 2013 IEEE International Conference on Big Data, 575-579, 2013 | 7 | 2013 |
| Mining data streams with dynamic confidence intervals D Trabold, T Horváth Big Data Analytics and Knowledge Discovery: 18th International Conference …, 2016 | 3 | 2016 |
| The dicode data mining services N Friesen, M Jakob, J Kindermann, D Maassen, A Poigné, S Rüping, ... Mastering Data-Intensive Collaboration and Decision Making: Research and …, 2014 | 3 | 2014 |
| Benefits of Unstructured Data for Industrial Quality Analysis C Hänig, M Schierle, D Trabold Intelligent Automation and Systems Engineering, 257-270, 2011 | 3 | 2011 |
| Effective approximation of parametrized closure systems over transactional data streams D Trabold, T Horváth, S Wrobel Machine Learning 109, 1147-1177, 2020 | 1 | 2020 |
| Mining Frequent Itemsets from Transactional Data Streams with Probabilistic Error Bounds D Trabold Rheinische Friedrich-Wilhelms-Universität Bonn, 2020 | 1 | 2020 |
| Quantum Machine Learning. Eine Analyse zu Kompetenz, Forschung und Anwendung C Bauckhage, E Brito, I Daase, L Franken, B Georgiev, D Hecker, ... Fraunhofer IAIS, 2020 | | 2020 |
| In-stream frequent itemset mining with output proportional memory footprint D Trabold, M Boley, M Mock, T Horváth Workshop on Knowledge Discovery, Data Mining and Machine Learning 2015, 93-104, 2015 | | 2015 |
| Model Management für Schema-Evolution D Trabold | | |
