| MOA: Massive Online Analysis, a framework for stream classification and clustering. A Bifet, G Holmes, B Pfahringer, P Kranen, H Kremer, T Jansen, T Seidl Journal of Machine Learning Research (JMLR), 2010 | 2025 | 2010 |
| An effective evaluation measure for clustering on evolving data streams H Kremer, P Kranen, T Jansen, T Seidl, A Bifet, G Holmes, B Pfahringer Proceedings of the 17th ACM SIGKDD international conference on Knowledge …, 2011 | 118 | 2011 |
| Anticipatory DTW for Efficient Similarity Search in Time Series Databases I Assent, M Wichterich, R Krieger, H Kremer, T Seidl Proceedings of the VLDB Endowment 2 (1), 826-837, 2009 | 86 | 2009 |
| MOA: a real-time analytics open source framework A Bifet, G Holmes, B Pfahringer, J Read, P Kranen, H Kremer, T Jansen, ... Machine Learning and Knowledge Discovery in Databases: European Conference …, 2011 | 66 | 2011 |
| Subspace Clustering for Uncertain Data S Günnemann, H Kremer, T Seidl Proceedings of the SDM Conference, 2010 | 39 | 2010 |
| Clustering performance on evolving data streams: Assessing algorithms and evaluation measures within moa P Kranen, H Kremer, T Jansen, T Seidl, A Bifet, G Holmes, B Pfahringer 2010 IEEE International Conference on Data Mining Workshops, 1400-1403, 2010 | 31 | 2010 |
| Stream data mining using the MOA framework P Kranen, H Kremer, T Jansen, T Seidl, A Bifet, G Holmes, B Pfahringer, ... Database Systems for Advanced Applications: 17th International Conference …, 2012 | 29 | 2012 |
| Tracing Evolving Subspace Clusters in Temporal Climate Data S Günnemann, H Kremer, C Laufkötter, T Seidl Data Mining and Knowledge Discovery Journal (DMKD) 24 (2), 387-410, 2012 | 24 | 2012 |
| Subspace clustering for indexing high dimensional data: a main memory index based on local reductions and individual multi-representations S Günnemann, H Kremer, D Lenhard, T Seidl Proceedings of the 14th International Conference on Extending Database …, 2011 | 19 | 2011 |
| Detecting climate change in multivariate time series data by novel clustering and cluster tracing techniques H Kremer, S Gunnemann, T Seidl 2010 IEEE International Conference on Data Mining Workshops, 96-97, 2010 | 19 | 2010 |
| An extension of the PMML standard to subspace clustering models S Günnemann, H Kremer, T Seidl Proceedings of the 2011 workshop on Predictive markup language modeling, 48-53, 2011 | 14 | 2011 |
| Efficient Processing of Multiple DTW Queries in Time Series Databases H Kremer, S Günnemann, AM Ivanescu, I Assent, T Seidl Scientific and Statistical Database Management (SSDBM), 150-167, 2011 | 12 | 2011 |
| CoDA: Interactive cluster based concept discovery S Günnemann, I Färber, H Kremer, T Seidl Proceedings of the VLDB Endowment 3 (1-2), 1633-1636, 2010 | 12 | 2010 |
| Tracing evolving clusters by subspace and value similarity S Günnemann, H Kremer, C Laufkötter, T Seidl Advances in Knowledge Discovery and Data Mining: 15th Pacific-Asia …, 2011 | 11 | 2011 |
| Robust adaptable video copy detection I Assent, H Kremer Advances in Spatial and Temporal Databases: 11th International Symposium …, 2009 | 11 | 2009 |
| A subspace clustering extension for the KNIME data mining framework S Günnemann, H Kremer, R Musiol, R Haag, T Seidl 2012 IEEE 12th International Conference on Data Mining Workshops, 886-889, 2012 | 8 | 2012 |
| Mining of temporal coherent subspace clusters in multivariate time series databases H Kremer, S Günnemann, A Held, T Seidl Advances in Knowledge Discovery and Data Mining: 16th Pacific-Asia …, 2012 | 8 | 2012 |
| MCExplorer: interactive exploration of multiple (Subspace) clustering solutions S Günnemann, H Kremer, I Färber, T Seidl 2010 IEEE International Conference on Data Mining Workshops, 1387-1390, 2010 | 8 | 2010 |
| Pattern detector: fast detection of suspicious stream patterns for immediate reaction I Assent, H Kremer, S Günnemann, T Seidl Proceedings of the 13th International Conference on Extending Database …, 2010 | 6 | 2010 |
| Effective and robust mining of temporal subspace clusters H Kremer, S Günnemann, A Held, T Seidl 2012 IEEE 12th International Conference on Data Mining, 369-378, 2012 | 5 | 2012 |
Thomas SeidlProfessor of Computer Science, Ludwig-Maximilians-Universität München (LMU Munich), MCML, GermanyDirección de correo verificada de dbs.ifi.lmu.de
