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ORCIDThomas Seidl 0001
Person information
- affiliation (since 2016): Ludwig Maximilians University of Munich, Institute for Computer Science, Germany
- affiliation (2002-2016): RWTH Aachen University, Germany
Other persons with the same name
- Thomas Seidl 0002 — Fraunhofer Institute for Factory Operation and Automation (IFF)
- Thomas Seidl 0003 — VfB Stuttgart, Stuttgart, Germany
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Books and Theses
- 1998
[b1]Thomas Seidl:
Adaptable similarity search in 3-D spatial database systems. Ludwig Maximilian University of Munich, Germany, Utz 1998, ISBN 978-3-89675-327-4, pp. 1-298
Journal Articles
- 2023
- [j47]Theresa Ullmann
, Anna Beer, Maximilian Hünemörder, Thomas Seidl, Anne-Laure Boulesteix:
Over-optimistic evaluation and reporting of novel cluster algorithms: an illustrative study. Adv. Data Anal. Classif. 17(1): 211-238 (2023) - 2022
- [j46]Ellen Hohma, Christian M. M. Frey, Anna Beer, Thomas Seidl:
SCAR - Spectral Clustering Accelerated and Robustified. Proc. VLDB Endow. 15(11): 3031-3044 (2022) - 2021
- [j45]Daniyal Kazempour, Johannes Winter, Peer Kröger, Thomas Seidl:
On Methods and Measures for the Inspection of Arbitrarily Oriented Subspace Clusters. Datenbank-Spektrum 21(3): 213-223 (2021) - 2020
- [j44]Florian Richter, Yifeng Lu, Daniyal Kazempour, Thomas Seidl:
"Show Me the Crowds!" Revealing Cluster Structures Through AMTICS. Data Sci. Eng. 5(4): 360-374 (2020) - [j43]Dietrich Trautmann, Michael Fromm, Volker Tresp, Thomas Seidl, Hinrich Schütze:
Relational and Fine-Grained Argument Mining. Datenbank-Spektrum 20(2): 99-105 (2020) - 2019
- [j42]Janis Held, Anna Beer, Thomas Seidl:
Chain-detection Between Clusters. Datenbank-Spektrum 19(3): 219-230 (2019) - [j41]Daniyal Kazempour, Markus Mauder, Peer Kröger, Thomas Seidl:
Detecting global hyperparaboloid correlated clusters: a Hough-transform based multicore algorithm. Distributed Parallel Databases 37(1): 39-72 (2019) - [j40]Marwan Hassani, Daniel Töws, Alfredo Cuzzocrea, Thomas Seidl:
BFSPMiner: an effective and efficient batch-free algorithm for mining sequential patterns over data streams. Int. J. Data Sci. Anal. 8(3): 223-239 (2019) - [j39]Florian Richter, Thomas Seidl:
Looking into the TESSERACT: Time-drifts in event streams using series of evolving rolling averages of completion times. Inf. Syst. 84: 265-282 (2019) - 2018
- [j38]Göran Kauermann, Thomas Seidl:
Data Science: a proposal for a curriculum. Int. J. Data Sci. Anal. 6(3): 195-199 (2018) - 2017
- [j37]Daniel Schüller, Christian Beecks, Marwan Hassani, Jennifer Hinnell, Bela Brenger, Thomas Seidl, Irene Mittelberg:
Automated Pattern Analysis in Gesture Research: Similarity Measuring in 3D Motion Capture Models of Communicative Action. Digit. Humanit. Q. 11(2) (2017) - [j36]Brigitte Boden, Stephan Günnemann, Holger Hoffmann, Thomas Seidl:
MiMAG: mining coherent subgraphs in multi-layer graphs with edge labels. Knowl. Inf. Syst. 50(2): 417-446 (2017) - [j35]Marwan Hassani, Thomas Seidl:
Using internal evaluation measures to validate the quality of diverse stream clustering algorithms. Vietnam. J. Comput. Sci. 4(3): 171-183 (2017) - 2016
- [j34]Christian Beecks, Marwan Hassani, Bela Brenger, Jennifer Hinnell, Daniel Schüller, Irene Mittelberg, Thomas Seidl:
Efficient Query Processing in 3D Motion Capture Gesture Databases. Int. J. Semantic Comput. 10(1): 5-26 (2016) - [j33]Marwan Hassani, Thomas Seidl:
Clustering Big Data streams: recent challenges and contributions. it Inf. Technol. 58(4): 206-213 (2016) - 2015
- [j32]Marwan Hassani, Yunsu Kim, Seungjin Choi, Thomas Seidl:
Subspace clustering of data streams: new algorithms and effective evaluation measures. J. Intell. Inf. Syst. 45(3): 319-335 (2015) - [j31]Emmanuel Müller, Ira Assent, Stephan Günnemann, Thomas Seidl, Jennifer G. Dy:
MultiClust special issue on discovering, summarizing and using multiple clusterings. Mach. Learn. 98(1-2): 1-5 (2015) - 2014
- [j30]Stephan Günnemann, Ines Färber, Brigitte Boden, Thomas Seidl:
GAMer: a synthesis of subspace clustering and dense subgraph mining. Knowl. Inf. Syst. 40(2): 243-278 (2014) - [j29]Christian Beecks, Steffen Kirchhoff, Thomas Seidl:
On stability of signature-based similarity measures for content-based image retrieval. Multim. Tools Appl. 71(1): 349-362 (2014) - 2013
- [j28]Thomas Seidl:
Datenmanagement und -exploration an der RWTH Aachen. Datenbank-Spektrum 13(1): 55-58 (2013) - [j27]Markus Harmsen, Benedikt Fischer, Hauke Schramm, Thomas Seidl, Thomas Martin Deserno:
Support Vector Machine Classification Based on Correlation Prototypes Applied to Bone Age Assessment. IEEE J. Biomed. Health Informatics 17(1): 190-197 (2013) - 2012
- [j26]Stephan Günnemann, Hardy Kremer, Charlotte Laufkötter, Thomas Seidl:
Tracing Evolving Subspace Clusters in Temporal Climate Data. Data Min. Knowl. Discov. 24(2): 387-410 (2012) - [j25]Stephan Günnemann, Brigitte Boden, Thomas Seidl:
Finding density-based subspace clusters in graphs with feature vectors. Data Min. Knowl. Discov. 25(2): 243-269 (2012) - [j24]Philipp Kranen, Ira Assent, Thomas Seidl:
An Index-Inspired Algorithm for Anytime Classification on Evolving Data Streams. Datenbank-Spektrum 12(1): 43-50 (2012) - [j23]Anca Maria Ivanescu, Marc Wichterich, Christian Beecks, Thomas Seidl:
The ClasSi coefficient for the evaluation of ranking quality in the presence of class similarities. Frontiers Comput. Sci. 6(5): 568-580 (2012) - [j22]Marwan Hassani, Thomas Seidl:
Distributed Weighted Clustering of Evolving Sensor Data Streams with Noise. J. Digit. Inf. Manag. 10(6): 410-420 (2012) - 2011
- [j21]Christoph Busch, Ulrike Korte, Sebastian Abt, Christian Böhm, Ines Färber, Sergej Fries, Johannes Merkle, Claudia Nickel, Alexander Nouak, Alexander Opel, Annahita Oswald, Thomas Seidl, Bianca Wackersreuther, Peter Wackersreuther, Xuebing Zhou:
Biometric Template Protection - Ein Bericht über das Projekt BioKeyS. Datenschutz und Datensicherheit 35(3): 183-191 (2011) - [j20]Nikos Mamoulis, Thomas Seidl:
Special section on spatial and temporal databases. GeoInformatica 15(4): 663-664 (2011) - [j19]Philipp Kranen, Ira Assent, Corinna Baldauf, Thomas Seidl:
The ClusTree: indexing micro-clusters for anytime stream mining. Knowl. Inf. Syst. 29(2): 249-272 (2011) - 2010
- [j18]Stephan Günnemann, Ines Färber, Hardy Kremer, Thomas Seidl:
CoDA: Interactive Cluster Based Concept Discovery. Proc. VLDB Endow. 3(2): 1633-1636 (2010) - 2009
- [j17]Philipp Kranen, Thomas Seidl:
Harnessing the strengths of anytime algorithms for constant data streams. Data Min. Knowl. Discov. 19(2): 245-260 (2009) - [j16]Matthias Schiffer, Emmanuel Müller, Thomas Seidl:
SubRank: Ranking Local Outliers in Projections of High-Dimensional Spaces. Datenbank-Spektrum 9(29): 53-55 (2009) - [j15]Ira Assent, Marc Wichterich, Ralph Krieger, Hardy Kremer, Thomas Seidl:
Anticipatory DTW for Efficient Similarity Search in Time Series Databases. Proc. VLDB Endow. 2(1): 826-837 (2009) - [j14]Emmanuel Müller, Stephan Günnemann, Ira Assent, Thomas Seidl:
Evaluating Clustering in Subspace Projections of High Dimensional Data. Proc. VLDB Endow. 2(1): 1270-1281 (2009) - 2008
- [j13]David Ruau, Corinna Kolárik, Heinz-Theodor Mevissen, Emmanuel Müller, Ira Assent, Ralph Krieger, Thomas Seidl, Martin Hofmann-Apitius, Martin Zenke:
Public microarray repository semantic annotation with ontologies employing text mining and expression profile correlation. BMC Bioinform. 9(S-10) (2008) - [j12]Thomas Martin Deserno, Mark Oliver Güld, Bartosz Plodowski, Klaus Spitzer, Berthold B. Wein, Henning Schubert, Hermann Ney, Thomas Seidl:
Extended Query Refinement for Medical Image Retrieval. J. Digit. Imaging 21(3): 280-289 (2008) - [j11]Ira Assent, Ralph Krieger, Boris Glavic, Thomas Seidl:
Clustering multidimensional sequences in spatial and temporal databases. Knowl. Inf. Syst. 16(1): 29-51 (2008) - 2007
- [j10]Mohammed J. Zaki, Markus Peters, Ira Assent, Thomas Seidl:
Clicks: An effective algorithm for mining subspace clusters in categorical datasets. Data Knowl. Eng. 60(1): 51-70 (2007) - [j9]Ira Assent, Ralph Krieger, Emmanuel Müller, Thomas Seidl:
VISA: visual subspace clustering analysis. SIGKDD Explor. 9(2): 5-12 (2007) - 2006
- [j8]Ira Assent, Marc Wichterich, Thomas Seidl:
Adaptable Distance Functions for Similarity-based Multimedia Retrieval. Datenbank-Spektrum 19: 23-31 (2006) - 2005
- [j7]Christoph Brochhaus, Jost Enderle, Achim Schlosser, Thomas Seidl, Knut Stolze:
Efficient interval management using object-relational database servers. Inform. Forsch. Entwickl. 20(3): 121-137 (2005) - 2002
- [j6]Christian Böhm, Hans-Peter Kriegel, Thomas Seidl:
Combining Approximation Techniques and Vector Quantization for Adaptable Similarity Search. J. Intell. Inf. Syst. 19(2): 207-230 (2002) - 2000
- [j5]Stefan Berchtold, Daniel A. Keim, Hans-Peter Kriegel, Thomas Seidl:
Indexing the Solution Space: A New Technique for Nearest Neighbor Search in High-Dimensional Space. IEEE Trans. Knowl. Data Eng. 12(1): 45-57 (2000) - 1999
- [j4]Rolf Backofen, François Bry, Peter Clote, Hans-Peter Kriegel, Thomas Seidl, Klaus U. Schulz:
Bioinformatik - Aktuelles Schlagwort. Inform. Spektrum 22(5): 376-378 (1999) - 1998
- [j3]Hans-Peter Kriegel, Thomas Seidl:
Approximation-Based Similarity Search for 3-D Surface Segments. GeoInformatica 2(2): 113-147 (1998) - [j2]Thomas Seidl:
Adaptable Similarity Search in 3-D Spatial Database Systems (Abstract). Datenbank Rundbr. 21: 96 (1998) - [j1]Mihael Ankerst, Hans-Peter Kriegel, Thomas Seidl:
A Multistep Approach for Shape Similarity Search in Image Databases. IEEE Trans. Knowl. Data Eng. 10(6): 996-1004 (1998)
Conference and Workshop Papers
- 2024
- [c306]Andrea Maldonado, Christian M. M. Frey, Gabriel Marques Tavares, Nikolina Rehwald, Thomas Seidl:
GEDI: Generating Event Data with Intentional Features for Benchmarking Process Mining. BPM 2024: 221-237 - [c305]Ludwig Zellner, Simon Rauch, Janina Sontheim, Thomas Seidl:
On Diverse and Precise Recommendations for Small and Medium-Sized Enterprises. PAKDD (5) 2024: 118-130 - [c304]Philipp Jahn, Christian M. M. Frey, Anna Beer, Collin Leiber, Thomas Seidl:
Data with Density-Based Clusters: A Generator for Systematic Evaluation of Clustering Algorithms. ECML/PKDD (7) 2024: 3-21 - [c303]Sandra Gilhuber, Anna Beer, Yunpu Ma, Thomas Seidl:
FALCUN: A Simple and Efficient Deep Active Learning Strategy. ECML/PKDD (3) 2024: 421-439 - 2023
- [c302]Rajat Koner, Tanveer Hannan, Suprosanna Shit, Sahand Sharifzadeh, Matthias Schubert, Thomas Seidl, Volker Tresp:
InstanceFormer: An Online Video Instance Segmentation Framework. AAAI 2023: 1188-1195 - [c301]Michael Fromm, Max Berrendorf, Evgeniy Faerman, Thomas Seidl:
Cross-Domain Argument Quality Estimation. ACL (Findings) 2023: 13435-13448 - [c300]David Winkel, Niklas Strauß, Matthias Schubert, Thomas Seidl:
Simplex Decomposition for Portfolio Allocation Constraints in Reinforcement Learning. ECAI 2023: 2655-2662 - [c299]Andrea Maldonado, Gabriel Marques Tavares, Rafael Seidi Oyamada, Paolo Ceravolo, Thomas Seidl:
FEEED: Feature Extraction from Event Data. ICPM Doctoral Consortium / Demo 2023 - [c298]Andrea Maldonado, Ludwig Zellner, Sven Strickroth, Thomas Seidl:
Process Mining Techniques for Collusion Detection in Online Exams. ICPM Workshops 2023: 336-348 - [c297]Yao Zhang, Yunpu Ma, Thomas Seidl, Volker Tresp:
Adaptive Multi-Resolution Attention with Linear Complexity. IJCNN 2023: 1-8 - [c296]David Winkel, Niklas Strauß, Matthias Schubert, Yunpu Ma, Thomas Seidl:
Constrained Portfolio Management Using Action Space Decomposition for Reinforcement Learning. PAKDD (2) 2023: 373-385 - [c295]Sandra Gilhuber, Julian Busch, Daniel Rotthues, Christian M. M. Frey, Thomas Seidl:
DiffusAL: Coupling Active Learning with Graph Diffusion for Label-Efficient Node Classification. ECML/PKDD (1) 2023: 75-91 - [c294]Sandra Gilhuber, Rasmus Hvingelby, Mang Ling Ada Fok, Thomas Seidl:
How to Overcome Confirmation Bias in Semi-Supervised Image Classification by Active Learning. ECML/PKDD (2) 2023: 330-347 - 2022
- [c293]Sandra Gilhuber, Philipp Jahn, Yunpu Ma, Thomas Seidl:
VERIPS: Verified Pseudo-label Selection for Deep Active Learning. ICDM 2022: 951-956 - [c292]Sandra Gilhuber, Max Berrendorf, Yunpu Ma, Thomas Seidl:
Accelerating Diversity Sampling for Deep Active Learning By Low-Dimensional Representations. IAL@PKDD/ECML 2022: 43-48 - [c291]David Winkel, Niklas Strauß, Matthias Schubert, Thomas Seidl:
Risk-Aware Reinforcement Learning for Multi-Period Portfolio Selection. ECML/PKDD (6) 2022: 185-200 - 2021
- [c290]Michael Fromm, Evgeniy Faerman, Max Berrendorf, Siddharth Bhargava, Ruoxia Qi, Yao Zhang, Lukas Dennert, Sophia Selle, Yang Mao, Thomas Seidl:
Argument Mining Driven Analysis of Peer-Reviews. AAAI 2021: 4758-4766 - [c289]Sandra Obermeier, Anna Beer, Florian Wahl, Thomas Seidl:
Cluster Flow - an Advanced Concept for Ensemble-Enabling, Interactive Clustering. BTW 2021: 175-194 - [c288]Daniyal Kazempour, Anna Beer, Melanie Oelker, Peer Kröger, Thomas Seidl:
Compound Segmentation via Clustering on Mol2Vec-based Embeddings. e-Science 2021: 60-69 - [c287]Michael Fromm, Max Berrendorf, Sandra Obermeier, Thomas Seidl, Evgeniy Faerman:
Diversity Aware Relevance Learning for Argument Search. ECIR (2) 2021: 264-271 - [c286]Anna Beer, Ekaterina Allerborn, Valentin Hartmann, Thomas Seidl:
KISS - A fast kNN-based Importance Score for Subspaces. EDBT 2021: 391-396 - [c285]Anna Wimbauer, Florian Richter, Thomas Seidl:
PErrCas: Process Error Cascade Mining in Trace Streams. ICPM Workshops 2021: 224-236 - [c284]Anna Beer, Lisa Stephan, Thomas Seidl:
LUCKe - Connecting Clustering and Correlation Clustering. ICDM (Workshops) 2021: 431-440 - [c283]Julian Busch, Maximilian Hünemörder, Janis Held, Peer Kröger, Thomas Seidl:
Implicit Hough Transform Neural Networks for Subspace Clustering. ICDM (Workshops) 2021: 441-448 - [c282]Ludwig Zellner, Janina Sontheim, Florian Richter, Gabriel Lindner, Thomas Seidl:
SCORER-Gap: Sequentially Correlated Rules for Event Recommendation Considering Gap Size. ICDM (Workshops) 2021: 925-934 - [c281]Andreas Lohrer, Anna Beer, Maximilian Archimedes Xaver Hünemörder, Jenny Lauterbach, Thomas Seidl, Peer Kröger:
AnyCORE - An Anytime Algorithm for Cluster Outlier REmoval. LWDA 2021: 145-156 - [c280]Julian Busch, Anton Kocheturov, Volker Tresp, Thomas Seidl:
NF-GNN: Network Flow Graph Neural Networks for Malware Detection and Classification. SSDBM 2021: 121-132 - 2020
- [c279]Florian Richter, Janina Sontheim, Ludwig Zellner, Thomas Seidl:
TADE: Stochastic Conformance Checking Using Temporal Activity Density Estimation. BPM 2020: 220-236 - [c278]Valentyn Melnychuk, Evgeniy Faerman, Ilja Manakov, Thomas Seidl:
Matching the Clinical Reality: Accurate OCT-Based Diagnosis From Few Labels. CIKM (Workshops) 2020 - [c277]Florian Richter, Yifeng Lu, Daniyal Kazempour, Thomas Seidl:
AMTICS: Aligning Micro-clusters to Identify Cluster Structures. DASFAA (1) 2020: 752-768 - [c276]Julian Busch, Jiaxing Pi, Thomas Seidl:
PushNet: Efficient and Adaptive Neural Message Passing. ECAI 2020: 1039-1046 - [c275]Max Berrendorf, Evgeniy Faerman, Valentyn Melnychuk, Volker Tresp, Thomas Seidl:
Knowledge Graph Entity Alignment with Graph Convolutional Networks: Lessons Learned. ECIR (2) 2020: 3-11 - [c274]Daniyal Kazempour, Peer Kröger, Thomas Seidl:
Towards an Internal Evaluation Measure for Arbitrarily Oriented Subspace Clustering. ICDM (Workshops) 2020: 300-307 - [c273]Daniyal Kazempour, Long Mathias Yan, Peer Kröger, Thomas Seidl:
You see a set of wagons - I see one train: Towards a unified view of local and global arbitrarily oriented subspace clusters. ICDM (Workshops) 2020: 308-315 - [c272]Daniyal Kazempour, Anna Beer, Peer Kröger, Thomas Seidl:
I fold you so! An internal evaluation measure for arbitrary oriented subspace clustering. ICDM (Workshops) 2020: 316-323 - [c271]Thomas Seidl:
Keynote Data Mining on Process Data. ICPM 2020: 1 - [c270]Florian Richter, Yifeng Lu, Ludwig Zellner, Janina Sontheim, Thomas Seidl:
TOAD: Trace Ordering for Anomaly Detection. ICPM 2020: 169-176 - [c269]Ludwig Zellner, Florian Richter, Janina Sontheim, Andrea Maldonado, Thomas Seidl:
Concept Drift Detection on Streaming Data with Dynamic Outlier Aggregation. ICPM Workshops 2020: 206-217 - [c268]Florian Richter, Andrea Maldonado, Ludwig Zellner, Thomas Seidl:
OTOSO: Online Trace Ordering for Structural Overviews. ICPM Workshops 2020: 218-229 - [c267]Andrea Maldonado, Janina Sontheim, Florian Richter, Thomas Seidl:
Performance Skyline: Inferring Process Performance Models from Interval Events. ICPM Workshops 2020: 230-242 - [c266]Yao Zhang, Yifeng Lu, Thomas Seidl:
KNNAC: An Efficient k Nearest Neighbor Based Clustering with Active Core Detection. iiWAS 2020: 62-71 - [c265]Yifeng Lu, Yao Zhang, Florian Richter, Thomas Seidl:
k-Nearest Neighbor based Clustering with Shape Alternation Adaptivity. IJCNN 2020: 1-8 - [c264]Anna Beer, Daniyal Kazempour, Julian Busch, Alexander Tekles, Thomas Seidl:
Grace - Limiting the Number of Grid Cells for Clustering High-Dimensional Data. LWDA 2020: 11-22 - [c263]Felix Borutta, Daniyal Kazempour, Felix Mathy, Peer Kröger, Thomas Seidl:
Detecting Arbitrarily Oriented Subspace Clusters in Data Streams Using Hough Transform. PAKDD (1) 2020: 356-368 - [c262]Anna Beer, Dominik Seeholzer, Nadine Sarah Schüler, Thomas Seidl:
Angle-Based Clustering. SISAP 2020: 312-320 - [c261]Anna Beer, Valentin Hartmann, Thomas Seidl:
Orderings of Data - More Than a Tripping Hazard: Visionary. SSDBM 2020: 17:1-17:4 - 2019
- [c260]Florian Richter, Ludwig Zellner, Imen Azaiz, David Winkel, Thomas Seidl:
LIProMa: Label-Independent Process Matching. Business Process Management Workshops 2019: 186-198 - [c259]Janis Held, Anna Beer, Thomas Seidl:
Chain-detection for DBSCAN. BTW (Workshops) 2019: 173-183 - [c258]Daniyal Kazempour, Maksim Kazakov, Peer Kröger, Thomas Seidl:
DICE: Density-based Interactive Clustering and Exploration. BTW 2019: 547-550 - [c257]Anna Beer, Daniyal Kazempour, Thomas Seidl:
Rock - Let the points roam to their clusters themselves. EDBT 2019: 630-633 - [c256]Daniyal Kazempour, Lisa Krombholz, Peer Kröger, Thomas Seidl:
A Galaxy of Correlations. EDBT 2019: 702-705 - [c255]Daniyal Kazempour, Thomas Seidl:
Insights into a running clockwork: On interactive process-aware clustering. EDBT 2019: 706-709 - [c254]Anna Beer, Daniyal Kazempour, Marcel Baur, Thomas Seidl:
Human Learning in Data Science. HCI (34) 2019: 170-176 - [c253]Daniyal Kazempour, Anna Beer, Thomas Seidl:
Data on RAILs: On Interactive Generation of Artificial Linear Correlated Data. HCI (34) 2019: 184-189 - [c252]Anna Beer, Nadine Sarah Schüler, Thomas Seidl:
A Generator for Subspace Clusters. LWDA 2019: 69-73 - [c251]Maximilian Archimedes Xaver Hünemörder, Anna Beer, Daniyal Kazempour, Thomas Seidl:
CODEC - Detecting Linear Correlations in Dense Clusters using coMAD-based PCA. LWDA 2019: 111-114 - [c250]Daniyal Kazempour, Anna Beer, Oliver Schrüfer, Thomas Seidl:
Clustering Trend Data Time-Series through Segmentation of FFT-decomposed Signal Constituents. LWDA 2019: 127-138 - [c249]Daniyal Kazempour, Long Mathias Yan, Thomas Seidl:
From Covariance to Comode in context of Principal Component Analysis. LWDA 2019: 139-143 - [c248]Florian Richter, Florian Wahl, Alona Sydorova, Thomas Seidl:
k-process: Model-Conformance-based Clustering of Process Instances. LWDA 2019: 161-172 - [c247]Janina Sontheim, Florian Richter, Thomas Seidl:
Temporal Deviations on Event Sequences. LWDA 2019: 173-177 - [c246]Florian Richter, Ludwig Zellner, Janina Sontheim, Thomas Seidl:
Model-Aware Clustering of Non-conforming Traces. OTM Conferences 2019: 193-200 - [c245]Anna Beer, Jennifer Lauterbach, Thomas Seidl:
MORe++: k-Means Based Outlier Removal on High-Dimensional Data. SISAP 2019: 188-202 - [c244]Maximilian Archimedes Xaver Hünemörder, Daniyal Kazempour, Peer Kröger, Thomas Seidl:
SIDEKICK: Linear Correlation Clustering with Supervised Background Knowledge. SISAP 2019: 221-230 - [c243]Daniyal Kazempour, Max Hünemörder, Thomas Seidl:
On coMADs and Principal Component Analysis. SISAP 2019: 273-280 - [c242]Anna Beer, Daniyal Kazempour, Lisa Stephan, Thomas Seidl:
LUCK- Linear Correlation Clustering Using Cluster Algorithms and a kNN based Distance Function. SSDBM 2019: 181-184 - [c241]Anna Beer, Thomas Seidl:
Graph Ordering and Clustering: A Circular Approach. SSDBM 2019: 185-188 - [c240]Daniyal Kazempour, Kilian Emmerig, Peer Kröger, Thomas Seidl:
Detecting Global Periodic Correlated Clusters in Event Series based on Parameter Space Transform. SSDBM 2019: 222-225 - [c239]Daniyal Kazempour, Thomas Seidl:
On systematic hyperparameter analysis through the example of subspace clustering. SSDBM 2019: 226-229 - [c238]Michael Fromm, Evgeniy Faerman, Thomas Seidl:
TACAM: Topic And Context Aware Argument Mining. WI 2019: 99-106 - [c237]Yifeng Lu, Florian Richter, Thomas Seidl:
LSCMiner: Efficient Low Support Closed Itemsets Mining. WISE 2019: 293-309 - 2018
- [c236]Yifeng Lu, Florian Richter, Thomas Seidl:
Efficient Infrequent Itemset Mining Using Depth-First and Top-Down Lattice Traversal. DASFAA (1) 2018: 908-915 - [c235]Yifeng Lu, Thomas Seidl:
Towards Efficient Closed Infrequent Itemset Mining Using Bi-Directional Traversing. DSAA 2018: 140-149 - [c234]Daniyal Kazempour, Anna Beer, Friederike Herzog, Daniel Kaltenthaler, Johannes-Y. Lohrer, Thomas Seidl:
FATBIRD: A Tool for Flight and Trajectories Analyses of Birds. eScience 2018: 75-82 - [c233]Daniyal Kazempour, Andrian Mörtlbauer, Peer Kröger, Thomas Seidl:
Mirror Mirror on the Wall, What is the Fairest Linear Parameter Space Representation of All? On Representations of Linear Parameter Space in Context of Clustering. LWDA 2018: 169-173 - [c232]Daniyal Kazempour, Thomas Seidl:
Identifying Entangled Data Points on Iteration Trajectories of Clusterings. LWDA 2018: 174-178 - [c231]Daniyal Kazempour, Kevin Bein, Peer Kröger, Thomas Seidl:
D-MASC: A Novel Search Strategy for Detecting Regions of Interest in Linear Parameter Space. SISAP 2018: 163-176 - [c230]Daniyal Kazempour, Anna Beer, Johannes-Y. Lohrer, Daniel Kaltenthaler, Thomas Seidl:
PARADISO: an interactive approach of parameter selection for the mean shift algorithm. SSDBM 2018: 26:1-26:4 - 2017
- [c229]Florian Richter, Thomas Seidl:
TESSERACT: Time-Drifts in Event Streams Using Series of Evolving Rolling Averages of Completion Times. BPM 2017: 289-305 - [c228]Thomas Seidl:
Multimedia Similarity Search. BTW (Workshops) 2017: 397 - [c227]Marwan Hassani, Daniel Töws, Thomas Seidl:
Understanding the bigger picture: batch-free exploration of streaming sequential patterns with accurate prediction. SAC 2017: 866-869 - [c226]Merih Seran Uysal, Kai Driessen, Tobias Brockhoff, Thomas Seidl:
Fast Similarity Search with the Earth Mover's Distance via Feasible Initialization and Pruning. SISAP 2017: 141-155 - [c225]Yifeng Lu, Marwan Hassani, Thomas Seidl:
Incremental Temporal Pattern Mining Using Efficient Batch-Free Stream Clustering. SSDBM 2017: 7:1-7:12 - [c224]Daniyal Kazempour, Markus Mauder, Peer Kröger, Thomas Seidl:
Detecting Global Hyperparaboloid Correlated Clusters Based on Hough Transform. SSDBM 2017: 31:1-31:6 - 2016
- [c223]Marwan Hassani, Pascal Spaus, Alfredo Cuzzocrea, Thomas Seidl:
I-HASTREAM: Density-Based Hierarchical Clustering of Big Data Streams and Its Application to Big Graph Analytics Tools. CCGrid 2016: 656-665 - [c222]Merih Seran Uysal, Daniel Sabinasz, Thomas Seidl:
Approximation-Based Efficient Query Processing with the Earth Mover's Distance. DASFAA (2) 2016: 165-180 - [c221]Merih Seran Uysal, Christian Beecks, Daniel Sabinasz, Jochen Schmücking, Thomas Seidl:
Efficient Query Processing using the Earth's Mover Distance in Video Databases. EDBT 2016: 389-400 - [c220]Marwan Hassani, Yifeng Lu, Thomas Seidl:
Towards an Efficient Ranking of Interval-Based Patterns. EDBT 2016: 688-689 - [c219]Christian Beecks, Merih Seran Uysal, Thomas Seidl:
Distance-based Multimedia Indexing. EDBT 2016: 722-723 - [c218]Tom De Nies, Christian Beecks, Fréderic Godin, Wesley De Neve, Grzegorz Stepien, Dörthe Arndt, Laurens De Vocht, Ruben Verborgh, Thomas Seidl, Erik Mannens, Rik Van de Walle:
Normalized Semantic Web Distance. ESWC 2016: 69-84 - [c217]Marwan Hassani, Yifeng Lu, Jens Wischnewsky, Thomas Seidl:
A geometric approach for mining sequential patterns in interval-based data streams. FUZZ-IEEE 2016: 2128-2135 - [c216]Laurens De Vocht, Christian Beecks, Ruben Verborgh, Erik Mannens, Thomas Seidl, Rik Van de Walle:
Effect of Heuristics on Serendipity in Path-Based Storytelling with Linked Data. HCI (4) 2016: 238-251 - [c215]Erik Scharwächter, Emmanuel Müller, Jonathan F. Donges, Marwan Hassani, Thomas Seidl:
Detecting Change Processes in Dynamic Networks by Frequent Graph Evolution Rule Mining. ICDM 2016: 1191-1196 - [c214]Klaus Schoeffmann, Christian Beecks, Mathias Lux, Merih Seran Uysal, Thomas Seidl:
Content-based retrieval in videos from laparoscopic surgery. Medical Imaging: Image-Guided Procedures 2016: 97861V - [c213]Roland Assam, Subramanyam Sathyanarayana, Thomas Seidl:
Infusing Geo-Recency Mixture Models for Effective Location Prediction in LBSN. SDM 2016: 855-863 - [c212]Tom De Nies, Christian Beecks, Fréderic Godin, Wesley De Neve, Grzegorz Stepien, Dörthe Arndt, Laurens De Vocht, Ruben Verborgh, Thomas Seidl, Erik Mannens, Rik Van de Walle:
A Distance-Based Approach for Semantic Dissimilarity in Knowledge Graphs. ICSC 2016: 254-257 - 2015
- [c211]Ayman Tarakji, Marwan Hassani, Lyubomir Georgiev, Thomas Seidl, Rainer Leupers:
Parallel Density-Based Stream Clustering Using a Multi-user GPU Scheduler. BDAS 2015: 343-360 - [c210]Christian Beecks, Merih Seran Uysal, Thomas Seidl:
Gradient-based signatures for big multimedia data. IEEE BigData 2015: 2834-2835 - [c209]Daniel Töws, Marwan Hassani, Christian Beecks, Thomas Seidl:
Optimizing Sequential Pattern Mining Within Multiple Streams. BTW Workshops 2015: 223-232 - [c208]Christian Beecks, Merih Seran Uysal, Thomas Seidl:
Distance-based Multimedia Indexing. BTW Workshops 2015: 265-268 - [c207]Volker Markl, Erhard Rahm, Wolfgang Lehner, Michael Beigl, Thomas Seidl:
Big Data-Zentren - Vorstellung und Panel. BTW 2015: 477-479 - [c206]Marwan Hassani, Christian Beecks, Daniel Töws, Tatiana Serbina, Max Haberstroh, Paula Niemietz, Sabina Jeschke, Stella Neumann, Thomas Seidl:
Sequential Pattern Mining of Multimodal Streams in the Humanities. BTW 2015: 683-686 - [c205]Merih Seran Uysal, Christian Beecks, Thomas Seidl:
On efficient content-based near-duplicate video detection. CBMI 2015: 1-6 - [c204]Merih Seran Uysal, Christian Beecks, Daniel Sabinasz, Thomas Seidl:
Large-scale Efficient and Effective Video Similarity Search. LSDS-IR@CIKM 2015: 3-8 - [c203]Christian Beecks, Merih Seran Uysal, Judith Hermanns, Thomas Seidl:
Gradient-based Signatures for Efficient Similarity Search in Large-scale Multimedia Databases. CIKM 2015: 1241-1250 - [c202]Laurens De Vocht, Christian Beecks, Ruben Verborgh, Thomas Seidl, Erik Mannens, Rik Van de Walle:
Improving Semantic Relatedness in Paths for Storytelling with Linked Data on the Web. ESWC (Satellite Events) 2015: 31-35 - [c201]Christian Beecks, Marwan Hassani, Florian Obeloer, Thomas Seidl:
Efficient Distance-Based Gestural Pattern Mining in Spatiotemporal 3D Motion Capture Databases. ICDM Workshops 2015: 1425-1432 - [c200]Christian Beecks, Klaus Schoeffmann, Mathias Lux, Merih Seran Uysal, Thomas Seidl:
Endoscopic Video Retrieval: A Signature-Based Approach for Linking Endoscopic Images with Video Segments. ISM 2015: 33-38 - [c199]Christian Beecks, Marwan Hassani, Florian Obeloer, Thomas Seidl:
Efficient Query Processing in 3D Motion Capture Databases via Lower Bound Approximation of the Gesture Matching Distance. ISM 2015: 148-153 - [c198]Christian Beecks, Merih Seran Uysal, Thomas Seidl:
Earth Mover's Distance vs. Quadratic form Distance: An Analytical and Empirical Comparison. ISM 2015: 233-236 - [c197]Merih Seran Uysal, Christian Beecks, Daniel Sabinasz, Thomas Seidl:
Effective Content-Based Near-Duplicate Video Detection. ISM 2015: 254-257 - [c196]Marwan Hassani, Pascal Spaus, Alfredo Cuzzocrea, Thomas Seidl:
Adaptive Stream Clustering Using Incremental Graph Maintenance. BigMine 2015: 49-64 - [c195]Marwan Hassani, Christian Beecks, Daniel Töws, Thomas Seidl:
Mining Sequential Patterns of Event Streams in a Smart Home Application. LWA 2015: 159-170 - [c194]Christian Beecks, Merih Seran Uysal, Thomas Seidl:
Content-Based Image Retrieval with Gaussian Mixture Models. MMM (1) 2015: 294-305 - [c193]Marwan Hassani, Thomas Seidl:
Internal Clustering Evaluation of Data Streams. PAKDD Workshops 2015: 198-209 - [c192]Roland Assam, Simon Feiden, Thomas Seidl:
UrbanHubble: Location Prediction and Geo-Social Analytics in LBSN. ECML/PKDD (3) 2015: 329-332 - [c191]Michael Hund, Michael Behrisch, Ines Färber, Michael Sedlmair, Tobias Schreck, Thomas Seidl, Daniel A. Keim:
Subspace Nearest Neighbor Search - Problem Statement, Approaches, and Discussion - Position Paper. SISAP 2015: 307-313 - [c190]Merih Seran Uysal, Christian Beecks, Daniel Sabinasz, Thomas Seidl:
FELICITY: A Flexible Video Similarity Search Framework Using the Earth Mover's Distance. SISAP 2015: 347-350 - [c189]Marwan Hassani, Sergio Siccha, Florian Richter, Thomas Seidl:
Efficient Process Discovery From Event Streams Using Sequential Pattern Mining. SSCI 2015: 1366-1373 - [c188]Christian Beecks, Marwan Hassani, Jennifer Hinnell, Daniel Schüller, Bela Brenger, Irene Mittelberg, Thomas Seidl:
Spatiotemporal Similarity Search in 3D Motion Capture Gesture Streams. SSTD 2015: 355-372 - [c187]Merih Seran Uysal, Christian Beecks, Jochen Schmücking, Thomas Seidl:
Efficient similarity search in scientific databases with feature signatures. SSDBM 2015: 30:1-30:12 - 2014
- [c186]Tom De Nies, Christian Beecks, Wesley De Neve, Thomas Seidl, Erik Mannens, Rik Van de Walle:
Towards Named-Entity-based Similarity Measures: Challenges and Opportunities. ESAIR 2014: 9-11 - [c185]Merih Seran Uysal, Christian Beecks, Thomas Seidl:
On Efficient Query Processing with the Earth Mover's Distance. PIKM@CIKM 2014: 25-32 - [c184]Merih Seran Uysal, Christian Beecks, Jochen Schmücking, Thomas Seidl:
Efficient Filter Approximation Using the Earth Mover's Distance in Very Large Multimedia Databases with Feature Signatures. CIKM 2014: 979-988 - [c183]Marwan Hassani, Thomas Seidl:
Efficient Streaming Detection of Hidden Clusters in Big Data Using Subspace Stream Clustering. DASFAA Workshops 2014: 146-160 - [c182]Sergej Fries, Stephan Wels, Thomas Seidl:
Projected Clustering for Huge Data Sets in MapReduce. EDBT 2014: 49-60 - [c181]Fréderic Godin, Tom De Nies, Christian Beecks, Laurens De Vocht, Wesley De Neve, Erik Mannens, Thomas Seidl, Rik Van de Walle:
The Normalized Freebase Distance. ESWC (Satellite Events) 2014: 218-221 - [c180]Roland Assam, Thomas Seidl:
Prediction of freezing of gait from Parkinson's Disease movement time series using conditional random fields. HealthGIS 2014: 11-20 - [c179]Sergej Fries, Brigitte Boden, Grzegorz Stepien, Thomas Seidl:
PHiDJ: Parallel similarity self-join for high-dimensional vector data with MapReduce. ICDE 2014: 796-807 - [c178]Roland Assam, Thomas Seidl:
Check-in Location Prediction Using Wavelets and Conditional Random Fields. ICDM 2014: 713-718 - [c177]Stephan Günnemann, Ines Färber, Matthias Sebastian Rüdiger, Thomas Seidl:
SMVC: semi-supervised multi-view clustering in subspace projections. KDD 2014: 253-262 - [c176]Christian Beecks, Steffen Kirchhoff, Thomas Seidl:
On the Stability of Signature-Based Distance Functions for Content-Based Image Retrieval. LWA 2014: 226 - [c175]Marwan Hassani, Pascal Spaus, Thomas Seidl:
Adaptive Multiple-Resolution Stream Clustering. MLDM 2014: 134-148 - [c174]Roland Assam, Thomas Seidl:
Effective Map Matching Using Curve Tangents and Hidden Markov Model. MSN 2014: 213-219 - [c173]Roland Assam, Thomas Seidl:
Context-based location clustering and prediction using conditional random fields. MUM 2014: 1-10 - [c172]Marwan Hassani, Ayman Tarakji, Lyubomir Georgiev, Thomas Seidl:
Parallel Implementation of a Density-Based Stream Clustering Algorithm Over a GPU Scheduling System. PAKDD Workshops 2014: 441-453 - [c171]Brigitte Boden, Martin Ester, Thomas Seidl:
Density-Based Subspace Clustering in Heterogeneous Networks. ECML/PKDD (1) 2014: 149-164 - [c170]Roland Assam, Marwan Hassani, Michael Brysch, Thomas Seidl:
(k, d)-core anonymity: structural anonymization of massive networks. SSDBM 2014: 17:1-17:12 - [c169]Anca Maria Zimmer, Philip Driessen, Philipp Kranen, Thomas Seidl:
Inverse predictions on continuous models in scientific databases. SSDBM 2014: 26:1-26:12 - [c168]Marwan Hassani, Philipp Kranen, Rajveer Saini, Thomas Seidl:
Subspace anytime stream clustering. SSDBM 2014: 37:1-37:4 - [c167]Roland Assam, Thomas Seidl:
Near-Optimal Activity Prediction through Efficient Wavelet Modulus Maxima Partitioning and Conditional Random Fields. UIC/ATC/ScalCom 2014: 236-243 - [c166]Roland Assam, Thomas Seidl:
Activity recognition from sensors using dyadic wavelets and Hidden Markov Model. WiMob 2014: 442-448 - 2013
- [c165]Thomas Seidl, Sergej Fries, Brigitte Boden:
MR-DSJ: Distance-Based Self-Join for Large-Scale Vector Data Analysis with MapReduce. BTW 2013: 37-56 - [c164]Christian Beecks, Merih Seran Uysal, Philip Driessen, Thomas Seidl:
Content-based exploration of multimedia databases. CBMI 2013: 59-64 - [c163]Brigitte Boden, Roman Haag, Thomas Seidl:
Detecting and exploring clusters in attributed graphs: a plugin for the gephi platform. CIKM 2013: 2505-2508 - [c162]Marwan Hassani, Yunsu Kim, Thomas Seidl:
Subspace MOA: Subspace Stream Clustering Evaluation Using the MOA Framework. DASFAA (2) 2013: 446-449 - [c161]Ayman Tarakji, Marwan Hassani, Stefan Lankes, Thomas Seidl:
Using a Multitasking GPU Environment for Content-Based Similarity Measures of Big Data. ICCSA (5) 2013: 181-196 - [c160]Stephan Günnemann, Ines Färber, Sebastian Raubach, Thomas Seidl:
Spectral Subspace Clustering for Graphs with Feature Vectors. ICDM 2013: 231-240 - [c159]Hardy Kremer, Stephan Günnemann, Arne Held, Thomas Seidl:
An Evaluation Framework for Temporal Subspace Clustering Approaches. ICDM Workshops 2013: 1089-1092 - [c158]Anca Maria Zimmer, Michael Kurze, Thomas Seidl:
Adaptive Model Tree for Streaming Data. ICDM 2013: 1319-1324 - [c157]Christoph Quix, Johannes Barnickel, Sandra Geisler, Marwan Hassani, Saim Kim, Xiang Li, Andreas Lorenz, Till Quadflieg, Thomas Gries, Matthias Jarke, Steffen Leonhardt, Ulrike Meyer, Thomas Seidl:
HealthNet: A System for Mobile and Wearable Health Information Management. IMMoA 2013: 36-43 - [c156]Daniel Haak, Jing Yu, Hendrik Simon, Hauke Schramm, Thomas Seidl, Thomas M. Deserno:
Bone age assessment using support vector regression with smart class mapping. Medical Imaging: Computer-Aided Diagnosis 2013: 86700A - [c155]Christian Beecks, Steffen Kirchhoff, Thomas Seidl:
Signature matching distance for content-based image retrieval. ICMR 2013: 41-48 - [c154]Stephan Günnemann, Brigitte Boden, Ines Färber, Thomas Seidl:
Efficient Mining of Combined Subspace and Subgraph Clusters in Graphs with Feature Vectors. PAKDD (1) 2013: 261-275 - [c153]Marwan Hassani, Yunsu Kim, Seungjin Choi, Thomas Seidl:
Effective Evaluation Measures for Subspace Clustering of Data Streams. PAKDD Workshops 2013: 342-353 - [c152]Brigitte Boden, Stephan Günnemann, Holger Hoffmann, Thomas Seidl:
RMiCS: a robust approach for mining coherent subgraphs in edge-labeled multi-layer graphs. SSDBM 2013: 23:1-23:12 - [c151]Hardy Kremer, Stephan Günnemann, Simon Wollwage, Thomas Seidl:
Nesting the earth mover's distance for effective cluster tracing. SSDBM 2013: 34:1-34:4 - [c150]Roland Assam, Thomas Seidl:
A Model for Context-Aware Location Identity Preservation Using Differential Privacy. TrustCom/ISPA/IUCC 2013: 346-353 - [c149]Roland Assam, Thomas Seidl:
BodyGuards: A Clairvoyant Location Predictor Using Frequent Neighbors and Markov Model. UIC/ATC 2013: 25-32 - [c148]Roland Assam, Thomas Seidl:
Private Map Matching: Realistic Private Route Cognition on Road Networks. UIC/ATC 2013: 178-185 - 2012
- [c147]Brigitte Boden, Stephan Günnemann, Thomas Seidl:
Tracing clusters in evolving graphs with node attributes. CIKM 2012: 2331-2334 - [c146]Philipp Kranen, Stephan Wels, Tim Rohlfs, Sebastian Raubach, Thomas Seidl:
A tool for automated evaluation of algorithms. CIKM 2012: 2692-2694 - [c145]Ira Assent, Philipp Kranen, Corinna Baldauf, Thomas Seidl:
AnyOut: Anytime Outlier Detection on Streaming Data. DASFAA (1) 2012: 228-242 - [c144]Philipp Kranen, Hardy Kremer, Timm Jansen, Thomas Seidl, Albert Bifet, Geoff Holmes, Bernhard Pfahringer, Jesse Read:
Stream Data Mining Using the MOA Framework. DASFAA (2) 2012: 309-313 - [c143]Anca Maria Ivanescu, Philipp Kranen, Manfred Smieschek, Philip Driessen, Thomas Seidl:
PA-Miner: Process Analysis Using Retrieval, Modeling, and Prediction. DASFAA (2) 2012: 319-322 - [c142]Roland Assam, Marwan Hassani, Thomas Seidl:
Differential private trajectory protection of moving objects. IWGS@SIGSPATIAL/GIS 2012: 68-77 - [c141]Roland Assam, Thomas Seidl:
TMC-pattern: holistic trajectory extraction, modeling and mining. BigSpatial@SIGSPATIAL 2012: 71-80 - [c140]Emmanuel Müller, Stephan Günnemann, Ines Färber, Thomas Seidl:
Discovering Multiple Clustering Solutions: Grouping Objects in Different Views of the Data. ICDE 2012: 1207-1210 - [c139]Hardy Kremer, Stephan Günnemann, Arne Held, Thomas Seidl:
Effective and Robust Mining of Temporal Subspace Clusters. ICDM 2012: 369-378 - [c138]Anca Maria Ivanescu, Thivaharan Albin, Dirk Abel, Thomas Seidl:
Employing the Principal Hessian Direction for Building Hinging Hyperplane Models. ICDM Workshops 2012: 481-485 - [c137]Stephan Günnemann, Hardy Kremer, Richard Musiol, Roman Haag, Thomas Seidl:
A Subspace Clustering Extension for the KNIME Data Mining Framework. ICDM Workshops 2012: 886-889 - [c136]Andrada Tatu, Fabian Maass, Ines Färber, Enrico Bertini, Tobias Schreck, Thomas Seidl, Daniel A. Keim:
Subspace search and visualization to make sense of alternative clusterings in high-dimensional data. IEEE VAST 2012: 63-72 - [c135]Stephan Günnemann, Ines Färber, Thomas Seidl:
Multi-view clustering using mixture models in subspace projections. KDD 2012: 132-140 - [c134]Stephan Günnemann, Ines Färber, Kittipat Virochsiri, Thomas Seidl:
Subspace correlation clustering: finding locally correlated dimensions in subspace projections of the data. KDD 2012: 352-360 - [c133]Brigitte Boden, Stephan Günnemann, Holger Hoffmann, Thomas Seidl:
Mining coherent subgraphs in multi-layer graphs with edge labels. KDD 2012: 1258-1266 - [c132]Christian Beecks, Thomas Seidl:
On Stability of Adaptive Similarity Measures for Content-Based Image Retrieval. MMM 2012: 346-357 - [c131]Roland Assam, Marwan Hassani, Thomas Seidl:
Differential Private Trajectory Obfuscation. MobiQuitous 2012: 139-151 - [c130]Marwan Hassani, Thomas Seidl:
Resource-Aware Distributed Clustering of Drifting Sensor Data Streams. NDT (1) 2012: 592-607 - [c129]Hardy Kremer, Stephan Günnemann, Arne Held, Thomas Seidl:
Mining of Temporal Coherent Subspace Clusters in Multivariate Time Series Databases. PAKDD (1) 2012: 444-455 - [c128]Thomas Seidl, Brigitte Boden, Sergej Fries:
CC-MR - Finding Connected Components in Huge Graphs with MapReduce. ECML/PKDD (1) 2012: 458-473 - [c127]Stephan Günnemann, Brigitte Boden, Thomas Seidl:
Substructure Clustering: A Novel Mining Paradigm for Arbitrary Data Types. SSDBM 2012: 280-297 - [c126]Philipp Kranen, Marwan Hassani, Thomas Seidl:
BT* - An Advanced Algorithm for Anytime Classification. SSDBM 2012: 298-315 - [c125]Anca Maria Ivanescu, Philipp Kranen, Thomas Seidl:
Hinging Hyperplane Models for Multiple Predicted Variables. SSDBM 2012: 431-448 - [c124]Marwan Hassani, Pascal Spaus, Mohamed Medhat Gaber, Thomas Seidl:
Density-Based Projected Clustering of Data Streams. SUM 2012: 311-324 - 2011
- [c123]Christian Böhm, Ines Färber, Sergej Fries, Ulrike Korte, Johannes Merkle, Annahita Oswald, Thomas Seidl, Bianca Wackersreuther, Peter Wackersreuther:
Efficient Database Techniques for Identification with Fuzzy Vault Templates. BIOSIG 2011: 115-126 - [c122]Marc Wichterich, Anca Maria Ivanescu, Thomas Seidl:
Feature-Based Graph Similarity with Co-Occurrence Histograms and the Earth Mover's Distance. BTW 2011: 135-146 - [c121]Emmanuel Müller, Ira Assent, Stephan Günnemann, Patrick Gerwert, Matthias Hannen, Timm Jansen, Thomas Seidl:
A Framework for Evaluation and Exploration of Clustering Algorithms in Subspaces of High Dimensional Databases. BTW 2011: 347-366 - [c120]Christian Böhm, Ines Färber, Sergej Fries, Ulrike Korte, Johannes Merkle, Annahita Oswald, Thomas Seidl, Bianca Wackersreuther, Peter Wackersreuther:
Filtertechniken für geschützte biometrische Datenbanken. BTW 2011: 379-389 - [c119]Thivaharan Albin, Peter Drews, Frank J. Hesseler, Anca Maria Ivanescu, Thomas Seidl, Dirk Abel:
A hybrid control approach for low temperature combustion engine control. CDC/ECC 2011: 6846-6851 - [c118]Emmanuel Müller, Ira Assent, Stephan Günnemann, Thomas Seidl:
Scalable density-based subspace clustering. CIKM 2011: 1077-1086 - [c117]Stephan Günnemann, Ines Färber, Emmanuel Müller, Ira Assent, Thomas Seidl:
External evaluation measures for subspace clustering. CIKM 2011: 1363-1372 - [c116]Martin Krulis, Jakub Lokoc, Christian Beecks, Tomás Skopal, Thomas Seidl:
Processing the signature quadratic form distance on many-core GPU architectures. CIKM 2011: 2373-2376 - [c115]Stephan Günnemann, Hardy Kremer, Dominik Lenhard, Thomas Seidl:
Subspace clustering for indexing high dimensional data: a main memory index based on local reductions and individual multi-representations. EDBT 2011: 237-248 - [c114]Roland Assam, Thomas Seidl:
Preserving privacy of moving objects via temporal clustering of spatio-temporal data streams. SPRINGL 2011: 9-16 - [c113]Christian Beecks, Anca Maria Ivanescu, Steffen Kirchhoff, Thomas Seidl:
Modeling image similarity by Gaussian mixture models and the Signature Quadratic Form Distance. ICCV 2011: 1754-1761 - [c112]Emmanuel Müller, Matthias Schiffer, Thomas Seidl:
Statistical selection of relevant subspace projections for outlier ranking. ICDE 2011: 434-445 - [c111]Stephan Günnemann, Emmanuel Müller, Sebastian Raubach, Thomas Seidl:
Flexible Fault Tolerant Subspace Clustering for Data with Missing Values. ICDM 2011: 231-240 - [c110]Christian Beecks, Thomas Seidl:
Analyzing the inner workings of the Signature Quadratic Form Distance. ICME 2011: 1-6 - [c109]Hardy Kremer, Philipp Kranen, Timm Jansen, Thomas Seidl, Albert Bifet, Geoff Holmes, Bernhard Pfahringer:
An effective evaluation measure for clustering on evolving data streams. KDD 2011: 868-876 - [c108]Stephan Günnemann, Brigitte Boden, Thomas Seidl:
Finding Density-Based Subspace Clusters in Graphs with Feature Vectors. LWA 2011: 20-27 - [c107]Marwan Hassani, Philipp Kranen, Thomas Seidl:
Noise-aware Concise Clustering of Streaming Sensor Data in a Logarithmic Time. LWA 2011: 97-105 - [c106]Marwan Hassani, Thomas Seidl:
Towards a Mobile Health Context Prediction: Sequential Pattern Mining in Multiple Streams. Mobile Data Management (2) 2011: 55-57 - [c105]Christian Beecks, Jakub Lokoc, Thomas Seidl, Tomás Skopal:
Indexing the signature quadratic form distance for efficient content-based multimedia retrieval. ICMR 2011: 24 - [c104]Christian Beecks, Anca Maria Ivanescu, Steffen Kirchhoff, Thomas Seidl:
Modeling multimedia contents through probabilistic feature signatures. ACM Multimedia 2011: 1433-1436 - [c103]Christian Beecks, Ira Assent, Thomas Seidl:
Content-Based Multimedia Retrieval in the Presence of Unknown User Preferences. MMM (1) 2011: 140-150 - [c102]Christian Beecks, Merih Seran Uysal, Thomas Seidl:
L2-Signature Quadratic Form Distance for Efficient Query Processing in Very Large Multimedia Databases. MMM (1) 2011: 381-391 - [c101]Anca Maria Ivanescu, Marc Wichterich, Thomas Seidl:
ClasSi: Measuring Ranking Quality in the Presence of Object Classes with Similarity Information. PAKDD Workshops 2011: 185-196 - [c100]Stephan Günnemann, Hardy Kremer, Charlotte Laufkötter, Thomas Seidl:
Tracing Evolving Clusters by Subspace and Value Similarity. PAKDD (2) 2011: 444-456 - [c99]Stephan Günnemann, Brigitte Boden, Thomas Seidl:
DB-CSC: A Density-Based Approach for Subspace Clustering in Graphs with Feature Vectors. ECML/PKDD (1) 2011: 565-580 - [c98]Albert Bifet, Geoff Holmes, Bernhard Pfahringer, Jesse Read, Philipp Kranen, Hardy Kremer, Timm Jansen, Thomas Seidl:
MOA: A Real-Time Analytics Open Source Framework. ECML/PKDD (3) 2011: 617-620 - [c97]Christian Beecks, Anca Maria Ivanescu, Thomas Seidl, Diana Martin, Philipp Pischke, Reinhold Kneer:
Applying similarity search for the investigation of the fuel injection process. SISAP 2011: 117-118 - [c96]Jakub Lokoc, Christian Beecks, Thomas Seidl, Tomás Skopal:
Parameterized earth mover's distance for efficient metric space indexing. SISAP 2011: 121-122 - [c95]Hardy Kremer, Stephan Günnemann, Anca Maria Ivanescu, Ira Assent, Thomas Seidl:
Efficient Processing of Multiple DTW Queries in Time Series Databases. SSDBM 2011: 150-167 - [c94]Philipp Kranen, Felix Reidl, Fernando Sánchez Villaamil, Thomas Seidl:
Hierarchical Clustering for Real-Time Stream Data with Noise. SSDBM 2011: 405-413 - 2010
- [c93]Emmanuel Müller, Matthias Schiffer, Thomas Seidl:
Adaptive outlierness for subspace outlier ranking. CIKM 2010: 1629-1632 - [c92]Christian Beecks, Merih Seran Uysal, Thomas Seidl:
Signature Quadratic Form Distance. CIVR 2010: 438-445 - [c91]Ira Assent, Hardy Kremer, Thomas Seidl:
Speeding Up Complex Video Copy Detection Queries. DASFAA (1) 2010: 307-321 - [c90]Emmanuel Müller, Philipp Kranen, Michael Nett, Felix Reidl, Thomas Seidl:
Air-Indexing on Error Prone Communication Channels. DASFAA (1) 2010: 505-519 - [c89]Ira Assent, Hardy Kremer, Stephan Günnemann, Thomas Seidl:
Pattern detector: fast detection of suspicious stream patterns for immediate reaction. EDBT 2010: 709-712 - [c88]Christian Beecks, Merih Seran Uysal, Thomas Seidl:
Efficient k-nearest neighbor queries with the Signature Quadratic Form Distance. ICDE Workshops 2010: 10-15 - [c87]Hardy Kremer, Stephan Günnemann, Thomas Seidl:
Detecting Climate Change in Multivariate Time Series Data by Novel Clustering and Cluster Tracing Techniques. ICDM Workshops 2010: 96-97 - [c86]Stephan Günnemann, Ines Färber, Brigitte Boden, Thomas Seidl:
Subspace Clustering Meets Dense Subgraph Mining: A Synthesis of Two Paradigms. ICDM 2010: 845-850 - [c85]Emmanuel Müller, Stephan Günnemann, Ines Färber, Thomas Seidl:
Discovering Multiple Clustering Solutions: Grouping Objects in Different Views of the Data. ICDM 2010: 1220 - [c84]Stephan Günnemann, Hardy Kremer, Ines Färber, Thomas Seidl:
MCExplorer: Interactive Exploration of Multiple (Subspace) Clustering Solutions. ICDM Workshops 2010: 1387-1390 - [c83]Philipp Kranen, Hardy Kremer, Timm Jansen, Thomas Seidl, Albert Bifet, Geoff Holmes, Bernhard Pfahringer:
Clustering Performance on Evolving Data Streams: Assessing Algorithms and Evaluation Measures within MOA. ICDM Workshops 2010: 1400-1403 - [c82]Christian Beecks, Thilo Stadelmann, Bernd Freisleben, Thomas Seidl:
Visual speaker model exploration. ICME 2010: 727-728 - [c81]Christian Beecks, Merih Seran Uysal, Thomas Seidl:
A comparative study of similarity measures for content-based multimedia retrieval. ICME 2010: 1552-1557 - [c80]Christian Beecks, Sascha Wiedenfeld, Thomas Seidl:
Improving the Efficiency of Content-Based Multimedia Exploration. ICPR 2010: 3163-3166 - [c79]Ira Assent, Philipp Kranen, Corinna Baldauf, Thomas Seidl:
Detecting outliers on arbitrary data streams using anytime approaches. StreamKDD@KDD 2010: 10-15 - [c78]Christian Beecks, Philip Driessen, Thomas Seidl:
Index support for content-based multimedia exploration. ACM Multimedia 2010: 999-1002 - [c77]Stephan Günnemann, Thomas Seidl:
Subgraph Mining on Directed and Weighted Graphs. PAKDD (2) 2010: 133-146 - [c76]Philipp Kranen, Ralph Krieger, Stefan Denker, Thomas Seidl:
Bulk Loading Hierarchical Mixture Models for Efficient Stream Classification. PAKDD (2) 2010: 325-334 - [c75]Emmanuel Müller, Matthias Schiffer, Patrick Gerwert, Matthias Hannen, Timm Jansen, Thomas Seidl:
SOREX: Subspace Outlier Ranking Exploration Toolkit. ECML/PKDD (3) 2010: 607-610 - [c74]Stephan Günnemann, Hardy Kremer, Thomas Seidl:
Subspace Clustering for Uncertain Data. SDM 2010: 385-396 - [c73]Christian Beecks, Merih Seran Uysal, Thomas Seidl:
Similarity matrix compression for efficient signature quadratic form distance computation. SISAP 2010: 109-114 - [c72]Philipp Kranen, Stephan Günnemann, Sergej Fries, Thomas Seidl:
MC-Tree: Improving Bayesian Anytime Classification. SSDBM 2010: 252-269 - [c71]Albert Bifet, Geoff Holmes, Bernhard Pfahringer, Philipp Kranen, Hardy Kremer, Timm Jansen, Thomas Seidl:
MOA: Massive Online Analysis, a Framework for Stream Classification and Clustering. WAPA 2010: 44-50 - 2009
- [c70]Marc Wichterich, Christian Beecks, Martin Sundermeyer, Thomas Seidl:
Relevance Feedback for the Earth Mover's Distance. Adaptive Multimedia Retrieval 2009: 72-86 - [c69]Ira Assent, Stephan Günnemann, Hardy Kremer, Thomas Seidl:
High-Dimensional Indexing for Multimedia Features. BTW 2009: 187-206 - [c68]Christian Beecks, Marc Wichterich, Thomas Seidl:
Metrische Anpassung der Earth Mover's Distanz zur Ähnlichkeitssuche in Multimedia-Datenbanken. BTW 2009: 207-216 - [c67]Stephan Günnemann, Emmanuel Müller, Ines Färber, Thomas Seidl:
Detection of orthogonal concepts in subspaces of high dimensional data. CIKM 2009: 1317-1326 - [c66]Marc Wichterich, Christian Beecks, Martin Sundermeyer, Thomas Seidl:
Exploring multimedia databases via optimization-based relevance feedback and the earth mover's distance. CIKM 2009: 1621-1624 - [c65]Babak Ahmadi, Marios Hadjieleftheriou, Thomas Seidl, Divesh Srivastava, Suresh Venkatasubramanian:
Type-based categorization of relational attributes. EDBT 2009: 84-95 - [c64]Thomas Seidl, Ira Assent, Philipp Kranen, Ralph Krieger, Jennifer Herrmann:
Indexing density models for incremental learning and anytime classification on data streams. EDBT 2009: 311-322 - [c63]Philipp Kranen, Ira Assent, Corinna Baldauf, Thomas Seidl:
Self-Adaptive Anytime Stream Clustering. ICDM 2009: 249-258 - [c62]Emmanuel Müller, Ira Assent, Stephan Günnemann, Ralph Krieger, Thomas Seidl:
Relevant Subspace Clustering: Mining the Most Interesting Non-redundant Concepts in High Dimensional Data. ICDM 2009: 377-386 - [c61]Marwan Hassani, Emmanuel Müller, Thomas Seidl:
EDISKCO: energy efficient distributed in-sensor-network k-center clustering with outliers. KDD Workshop on Knowledge Discovery from Sensor Data 2009: 39-48 - [c60]Christian Beecks, Merih Seran Uysal, Thomas Seidl:
Signature quadratic form distances for content-based similarity. ACM Multimedia 2009: 697-700 - [c59]Philipp Kranen, Thomas Seidl:
Harnessing the Strengths of Anytime Algorithms for Constant Data Streams. ECML/PKDD (1) 2009: 31 - [c58]Emmanuel Müller, Ira Assent, Ralph Krieger, Stephan Günnemann, Thomas Seidl:
DensEst: Density Estimation for Data Mining in High Dimensional Spaces. SDM 2009: 175-186 - [c57]Emmanuel Müller, Ira Assent, Thomas Seidl:
HSM: Heterogeneous Subspace Mining in High Dimensional Data. SSDBM 2009: 497-516 - [c56]Philipp Kranen, Thomas Seidl:
Using Index Structures for Anytime Stream Mining. VLDB PhD Workshop 2009 - 2008
- [c55]Ira Assent, Ralph Krieger, Emmanuel Müller, Thomas Seidl:
EDSC: efficient density-based subspace clustering. CIKM 2008: 1093-1102 - [c54]Ira Assent, Ralph Krieger, Farzad Afschari, Thomas Seidl:
The TS-tree: efficient time series search and retrieval. EDBT 2008: 252-263 - [c53]Ira Assent, Marc Wichterich, Tobias Meisen, Thomas Seidl:
Efficient similarity search using the Earth Mover's Distance for large multimedia databases. ICDE 2008: 307-316 - [c52]Marc Wichterich, Christian Beecks, Thomas Seidl:
Ranking multimedia databases via relevance feedback with history and foresight support. ICDE Workshops 2008: 596-599 - [c51]Emmanuel Müller, Ira Assent, Uwe Steinhausen, Thomas Seidl:
OutRank: ranking outliers in high dimensional data. ICDE Workshops 2008: 600-603 - [c50]Ira Assent, Ralph Krieger, Emmanuel Müller, Thomas Seidl:
INSCY: Indexing Subspace Clusters with In-Process-Removal of Redundancy. ICDM 2008: 719-724 - [c49]Emmanuel Müller, Ira Assent, Ralph Krieger, Timm Jansen, Thomas Seidl:
Morpheus: interactive exploration of subspace clustering. KDD 2008: 1089-1092 - [c48]Philipp Kranen, David Kensche, Saim Kim, Nadine Zimmermann, Emmanuel Müller, Christoph Quix, Xiang Li, Thomas Gries, Thomas Seidl, Matthias Jarke, Steffen Leonhardt:
Mobile Mining and Information Management in HealthNet Scenarios. MDM 2008: 215-216 - [c47]Ira Assent, Ralph Krieger, Petra Welter, Jörg Herbers, Thomas Seidl:
SubClass: Classification of Multidimensional Noisy Data Using Subspace Clusters. PAKDD 2008: 40-52 - [c46]Ira Assent, Emmanuel Müller, Ralph Krieger, Timm Jansen, Thomas Seidl:
Pleiades: Subspace Clustering and Evaluation. ECML/PKDD (2) 2008: 666-671 - [c45]Marc Wichterich, Ira Assent, Philipp Kranen, Thomas Seidl:
Efficient EMD-based similarity search in multimedia databases via flexible dimensionality reduction. SIGMOD Conference 2008: 199-212 - [c44]Christoph Brochhaus, Thomas Seidl:
IndeGSRI: Efficient View-Dependent Ranking in CFD Post- processing Queries with RDBMS. SSDBM 2008: 598-604 - 2007
- [c43]Ira Assent, Ralph Krieger, Thomas Seidl:
AttentionAttractor: efficient video stream similarity query processing in real time. ICDE 2007: 1509-1510 - [c42]Ira Assent, Ralph Krieger, Emmanuel Müller, Thomas Seidl:
DUSC: Dimensionality Unbiased Subspace Clustering. ICDM 2007: 409-414 - [c41]Christoph Brochhaus, Thomas Seidl:
Efficient Index Support for View-Dependent Queries on CFD Data. SSTD 2007: 57-74 - [c40]Christoph Brochhaus, Thomas Seidl:
IndeGS: Index Supported Graphics Data Server for CFD Data Postprocessing. VLDB 2007: 1354-1357 - 2006
- [c39]Christoph Brochhaus, Marc Wichterich, Thomas Seidl:
Approximation Techniques to Enable Dimensionality Reduction for Voronoi-Based Nearest Neighbor Search. EDBT 2006: 204-221 - [c38]Ira Assent, Andrea Wenning, Thomas Seidl:
Approximation Techniques for Indexing the Earth Mover's Distance in Multimedia Databases. ICDE 2006: 11 - [c37]Ira Assent, Ralph Krieger, Boris Glavic, Thomas Seidl:
Spatial Multidimensional Sequence Clustering. ICDM Workshops 2006: 343-348 - [c36]Christian Thies, Marcel Schmidt-Borreda, Thomas Seidl, Thomas Martin Lehmann:
A classification framework for content-based extraction of biomedical objects from hierarchically decomposed images. Medical Imaging: Image Processing 2006: 61441N - 2005
- [c35]Christoph Brochhaus, Jost Enderle, Achim Schlosser, Thomas Seidl, Knut Stolze:
Integrating the Relational Interval Tree into IBM's DB2 Universal Database Server. BTW 2005: 67-86 - [c34]Mohammed Javeed Zaki, Markus Peters, Ira Assent, Thomas Seidl:
CLICKS: an effective algorithm for mining subspace clusters in categorical datasets. KDD 2005: 736-742 - [c33]Thomas Martin Lehmann, Daniel Beier, Christian Thies, Thomas Seidl:
Segmentation of medical images combining local, regional, global, and hierarchical distances into a bottom-up region merging scheme. Medical Imaging: Image Processing 2005 - [c32]Jost Enderle, Nicole Schneider, Thomas Seidl:
Efficiently Processing Queries on Interval-and-Value Tuples in Relational Databases. VLDB 2005: 385-396 - 2004
- [c31]Thomas Seidl:
Nearest Neighbor Search on Multimedia Indexing Structures. CVDB 2004: 2 - [c30]M. Tamer Özsu, Jean Carrive, Sébastien Gilles, Izabela Grasland, Roger Mohr, Thomas Seidl:
CVDB 2004 Panel: Future Applications and Solutions. CVDB 2004: 67-68 - [c29]Hans-Peter Kriegel, Martin Pfeifle, Marco Pötke, Thomas Seidl:
A Cost Model for Spatial Intersection Queries on RI-Trees. DASFAA 2004: 331-338 - [c28]Karin Kailing, Hans-Peter Kriegel, Stefan Schönauer, Thomas Seidl:
Efficient Similarity Search for Hierarchical Data in Large Databases. EDBT 2004: 676-693 - [c27]Karin Kailing, Hans-Peter Kriegel, Stefan Schönauer, Thomas Seidl:
Efficient Similarity Search in Large Databases of Tree Structured Objects. ICDE 2004: 835 - [c26]Jost Enderle, Matthias Hampel, Thomas Seidl:
Joining Interval Data in Relational Databases. SIGMOD Conference 2004: 683-694 - 2003
- [c25]Hans-Peter Kriegel, Martin Pfeifle, Marco Pötke, Matthias Renz, Thomas Seidl:
Spatial Data Management for Virtual Product Development. Computer Science in Perspective 2003: 216-230 - [c24]Hans-Peter Kriegel, Martin Pfeifle, Marco Pötke, Thomas Seidl:
The Paradigm of Relational Indexing: a Survey. BTW 2003: 285-304 - [c23]Hans-Peter Kriegel, Martin Pfeifle, Marco Pötke, Thomas Seidl:
Spatial Query Processing for High Resolutions. DASFAA 2003: 17-26 - [c22]Hans-Peter Kriegel, Peer Kröger, Zahi Mashael, Martin Pfeifle, Marco Pötke, Thomas Seidl:
Effective Similarity Search on Voxelized CAD Object. DASFAA 2003: 27-36 - 2002
- [c21]Hans-Peter Kriegel, Martin Pfeifle, Marco Pötke, Thomas Seidl:
A Cost Model for Interval Intersection Queries on RI-Trees. SSDBM 2002: 131-141 - 2001
- [c20]Christian Böhm, Hans-Peter Kriegel, Thomas Seidl:
Adaptable Similarity Search Using Vector Quantization. DaWaK 2001: 317-327 - [c19]Hans-Peter Kriegel, Andreas Müller, Marco Pötke, Thomas Seidl:
DIVE: Database Integration for Virtual Engineering. ICDE Demo Sessions 2001: 15-16 - [c18]Hans-Peter Kriegel, Andreas Müller, Marco Pötke, Thomas Seidl:
Spatial Data Management for Computer Aided Design. SIGMOD Conference 2001: 614 - [c17]Hans-Peter Kriegel, Marco Pötke, Thomas Seidl:
Interval Sequences: An Object-Relational Approach to Manage Spatial Data. SSTD 2001: 481-501 - [c16]Hans-Peter Kriegel, Marco Pötke, Thomas Seidl:
Object-Relational Indexing for General Interval Relationships. SSTD 2001: 522-542 - 2000
- [c15]Hans-Peter Kriegel, Marco Pötke, Thomas Seidl:
Managing Intervals Efficiently in Object-Relational Databases. VLDB 2000: 407-418 - 1999
- [c14]Thomas Seidl, Hans-Peter Kriegel:
Adaptable Similarity Search in Large Image Databases. State-of-the-Art in Content-Based Image and Video Retrieval 1999: 297-317 - [c13]Mihael Ankerst, Gabi Kastenmüller, Hans-Peter Kriegel, Thomas Seidl:
Nearest Neighbor Classification in 3D Protein Databases. ISMB 1999: 34-43 - [c12]Mihael Ankerst, Gabi Kastenmüller, Hans-Peter Kriegel, Thomas Seidl:
3D Shape Histograms for Similarity Search and Classification in Spatial Databases. SSD 1999: 207-226 - 1998
- [c11]Thomas Seidl, Gabi Kastenmüller, Hans-Peter Kriegel:
Similarity Search in 3D Protein Databases. German Conference on Bioinformatics 1998 - [c10]Stefan Berchtold, Bernhard Ertl, Daniel A. Keim, Hans-Peter Kriegel, Thomas Seidl:
Fast Nearest Neighbor Search in High-Dimensional Space. ICDE 1998: 209-218 - [c9]Thomas Seidl, Hans-Peter Kriegel:
Optimal Multi-Step k-Nearest Neighbor Search. SIGMOD Conference 1998: 154-165 - [c8]Mihael Ankerst, Bernhard Braunmüller, Hans-Peter Kriegel, Thomas Seidl:
Improving Adaptable Similarity Query Processing by Using Approximations. VLDB 1998: 206-217 - 1997
- [c7]Hans-Peter Kriegel, Thomas Schmidt, Thomas Seidl:
3D Similarity Search by Shape Approximation. SSD 1997: 11-28 - [c6]Thomas Seidl, Hans-Peter Kriegel:
Efficient User-Adaptable Similarity Search in Large Multimedia Databases. VLDB 1997: 506-515 - 1995
- [c5]Martin Ester, Hans-Peter Kriegel, Thomas Seidl, Xiaowei Xu:
Formbasierte Suche nach komplementären 3D-Oberflächen in einer Protein-Datenbank. BTW 1995: 373-382 - [c4]Thomas Seidl, Hans-Peter Kriegel:
Solvent Accessible Surface Representation in a Database System for Protein Docking. ISMB 1995: 350-358 - [c3]Thomas Seidl, Hans-Peter Kriegel:
A 3D Molecular Surface Representation Supporting Neighborhood Queries. SSD 1995: 240-258 - 1994
- [c2]Daniel A. Keim, Hans-Peter Kriegel, Thomas Seidl:
Supporting Data Mining of Large Databases by Visual Feedback Queries. ICDE 1994: 302-313 - 1993
- [c1]Daniel A. Keim, Hans-Peter Kriegel, Thomas Seidl:
Visual Feedback in Querying Large Databases. IEEE Visualization 1993: 158-165
Parts in Books or Collections
- 2020
- [p5]Yifeng Lu, Florian Richter, Thomas Seidl:
Efficient Infrequent Pattern Mining Using Negative Itemset Tree. Complex Pattern Mining 2020: 1-16 - 2014
- [p4]Ines Färber, Sergej Fries, Götz Marczinski, Thomas Seidl, Nils-Per Steinmann:
Machining Intelligence Network: Data Mining and Semantic Search in Manufacturing Industry. Towards the Internet of Services 2014: 417-424 - 2011
- [p3]Thomas Seidl, Jost Enderle:
Binary Search. Algorithms Unplugged 2011: 5-11 - 2008
- [p2]Thomas Seidl, Jost Enderle:
Binäre Suche. Taschenbuch der Algorithmen 2008: 7-13 - 2005
- [p1]Hans-Peter Kriegel, Martin Pfeifle, Marco Pötke, Thomas Seidl, Jost Enderle:
Object-Relational Spatial Indexing. Spatial Databases 2005: 49-80
Editorship
- 2021
- [e9]Thomas Seidl, Michael Fromm, Sandra Obermeier:
Proceedings of the LWDA 2021 Workshops: FGWM, KDML, FGWI-BIA, and FGIR, Online, September 1-3, 2021. CEUR Workshop Proceedings 2993, CEUR-WS.org 2021 [contents] - 2017
- [e8]Christian Beecks, Felix Borutta, Peer Kröger, Thomas Seidl:
Similarity Search and Applications - 10th International Conference, SISAP 2017, Munich, Germany, October 4-6, 2017, Proceedings. Lecture Notes in Computer Science 10609, Springer 2017, ISBN 978-3-319-68473-4 [contents] - 2015
- [e7]Thomas Seidl, Norbert Ritter, Harald Schöning, Kai-Uwe Sattler, Theo Härder, Steffen Friedrich, Wolfram Wingerath:
Datenbanksysteme für Business, Technologie und Web (BTW), 16. Fachtagung des GI-Fachbereichs "Datenbanken und Informationssysteme" (DBIS), 4.-6.3.2015 in Hamburg, Germany. Proceedings. LNI P-241, GI 2015, ISBN 978-3-88579-635-0 [contents] - 2014
- [e6]Thomas Seidl, Marwan Hassani, Christian Beecks:
Proceedings of the 16th LWA Workshops: KDML, IR and FGWM, Aachen, Germany, September 8-10, 2014. CEUR Workshop Proceedings 1226, CEUR-WS.org 2014 [contents] - 2012
- [e5]Emmanuel Müller, Thomas Seidl, Suresh Venkatasubramanian, Arthur Zimek:
3rd MultiClust Workshop: Discovering, Summarizing and Using Multiple Clusterings, MultiClust '12, in conjunction with SDM 2012, Anaheim, CA, USA, April 28, 2012, Anaheim, CA, USA, April 28, 2012. SIAM 2012 [contents] - 2011
- [e4]Emmanuel Müller, Stephan Günnemann, Ira Assent, Thomas Seidl:
Proceedings of the 2nd MultiClust Workshop: Discovering, Summarizing and Using Multiple Clusterings, Athens, Greece, September 5, 2011, in conjunction with ECML/PKDD 2011. CEUR Workshop Proceedings 772, CEUR-WS.org 2011 [contents] - 2009
- [e3]Nikos Mamoulis, Thomas Seidl, Torben Bach Pedersen, Kristian Torp, Ira Assent:
Advances in Spatial and Temporal Databases, 11th International Symposium, SSTD 2009, Aalborg, Denmark, July 8-10, 2009, Proceedings. Lecture Notes in Computer Science 5644, Springer 2009, ISBN 978-3-642-02981-3 [contents] - 2007
- [e2]Alfons Kemper, Harald Schöning, Thomas Rose, Matthias Jarke, Thomas Seidl, Christoph Quix, Christoph Brochhaus:
Datenbanksysteme in Business, Technologie und Web (BTW 2007), 12. Fachtagung des GI-Fachbereichs "Datenbanken und Informationssysteme" (DBIS), Proceedings, 7.-9. März 2007, Aachen, Germany. LNI P-103, GI 2007, ISBN 978-3-88579-197-3 [contents] - [e1]Matthias Jarke, Thomas Seidl, Christoph Quix, David Kensche, Stefan Conrad, Erhard Rahm, Ralf Klamma, Harald Kosch, Michael Granitzer, Sven Apel, Marko Rosenmüller, Gunter Saake, Olaf Spinczyk:
Datenbanksysteme in Business, Technologie und Web (BTW 2007), Workshop Proceedings, 5.-6. März 2007, Aachen, Germany. Verlagshaus Mainz, Aachen 2007, ISBN 3-86130-929-7 [contents]
Reference Works
- 2018
- [r2]Thomas Seidl:
Nearest Neighbor Classification. Encyclopedia of Database Systems (2nd ed.) 2018 - 2009
- [r1]Thomas Seidl:
Nearest Neighbor Classification. Encyclopedia of Database Systems 2009: 1885-1890
Data and Artifacts
- 2020
- [d1]Michael Fromm, Max Berrendorf, Evgheniy Faerman, Thomas Seidl:
Argument Mining Driven Analysis of Peer-Reviews Dataset. Zenodo, 2020
Informal and Other Publications
- 2024
- [i23]David Winkel, Niklas Strauß, Matthias Schubert, Thomas Seidl:
Simplex Decomposition for Portfolio Allocation Constraints in Reinforcement Learning. CoRR abs/2404.10683 (2024) - [i22]Valentin Margraf, Marcel Wever, Sandra Gilhuber, Gabriel Marques Tavares, Thomas Seidl, Eyke Hüllermeier:
ALPBench: A Benchmark for Active Learning Pipelines on Tabular Data. CoRR abs/2406.17322 (2024) - 2023
- [i21]Tanveer Hannan, Rajat Koner, Maximilian Bernhard, Suprosanna Shit, Bjoern H. Menze, Volker Tresp, Matthias Schubert, Thomas Seidl:
GRAtt-VIS: Gated Residual Attention for Auto Rectifying Video Instance Segmentation. CoRR abs/2305.17096 (2023) - [i20]Sandra Gilhuber, Julian Busch, Daniel Rotthues, Christian M. M. Frey, Thomas Seidl:
DiffusAL: Coupling Active Learning with Graph Diffusion for Label-Efficient Node Classification. CoRR abs/2308.00146 (2023) - [i19]Sandra Gilhuber, Rasmus Hvingelby, Mang Ling Ada Fok, Thomas Seidl:
How To Overcome Confirmation Bias in Semi-Supervised Image Classification By Active Learning. CoRR abs/2308.08224 (2023) - [i18]Tanveer Hannan, Md Mohaiminul Islam, Thomas Seidl, Gedas Bertasius:
RGNet: A Unified Retrieval and Grounding Network for Long Videos. CoRR abs/2312.06729 (2023) - 2022
- [i17]Michael Fromm, Max Berrendorf, Johanna Reiml, Isabelle Mayerhofer, Siddharth Bhargava, Evgeniy Faerman, Thomas Seidl:
Towards a Holistic View on Argument Quality Prediction. CoRR abs/2205.09803 (2022) - [i16]Rajat Koner, Tanveer Hannan, Suprosanna Shit, Sahand Sharifzadeh, Matthias Schubert, Thomas Seidl, Volker Tresp:
InstanceFormer: An Online Video Instance Segmentation Framework. CoRR abs/2208.10547 (2022) - 2021
- [i15]Julian Busch, Anton Kocheturov, Volker Tresp, Thomas Seidl:
NF-GNN: Network Flow Graph Neural Networks for Malware Detection and Classification. CoRR abs/2103.03939 (2021) - [i14]Yao Zhang, Yunpu Ma, Thomas Seidl, Volker Tresp:
Adaptive Multi-Resolution Attention with Linear Complexity. CoRR abs/2108.04962 (2021) - [i13]Nataliia Kees, Michael Fromm, Evgeniy Faerman, Thomas Seidl:
Active Learning for Argument Strength Estimation. CoRR abs/2109.11319 (2021) - 2020
- [i12]Julian Busch, Jiaxing Pi, Thomas Seidl:
PushNet: Efficient and Adaptive Neural Message Passing. CoRR abs/2003.02228 (2020) - [i11]Julian Busch, Evgeniy Faerman, Matthias Schubert, Thomas Seidl:
Learning Self-Expression Metrics for Scalable and Inductive Subspace Clustering. CoRR abs/2009.12875 (2020) - [i10]Valentyn Melnychuk, Evgeniy Faerman, Ilja Manakov, Thomas Seidl:
Matching the Clinical Reality: Accurate OCT-Based Diagnosis From Few Labels. CoRR abs/2010.12316 (2020) - [i9]Michael Fromm, Max Berrendorf, Sandra Obermeier, Thomas Seidl, Evgeniy Faerman:
Diversity Aware Relevance Learning for Argument Search. CoRR abs/2011.02177 (2020) - [i8]Michael Fromm, Evgeniy Faerman, Max Berrendorf, Siddharth Bhargava, Ruoxia Qi, Yao Zhang, Lukas Dennert, Sophia Selle, Yang Mao, Thomas Seidl:
Argument Mining Driven Analysis of Peer-Reviews. CoRR abs/2012.07743 (2020) - 2019
- [i7]Michael Fromm, Evgeniy Faerman, Thomas Seidl:
TACAM: Topic And Context Aware Argument Mining. CoRR abs/1906.00923 (2019) - [i6]Max Berrendorf, Evgeniy Faerman, Valentyn Melnychuk, Volker Tresp, Thomas Seidl:
Knowledge Graph Entity Alignment with Graph Convolutional Networks: Lessons Learned. CoRR abs/1911.08342 (2019) - 2014
- [i5]Stephan Günnemann, Hardy Kremer, Matthias Hannen, Thomas Seidl:
KDD-SC: Subspace Clustering Extensions for Knowledge Discovery Frameworks. CoRR abs/1407.3850 (2014) - 2012
- [i4]Daniel A. Keim, Fabrice Rossi, Thomas Seidl, Michel Verleysen, Stefan Wrobel:
Information Visualization, Visual Data Mining and Machine Learning (Dagstuhl Seminar 12081). Dagstuhl Reports 2(2): 58-83 (2012) - 2008
- [i3]Thomas Seidl, Emmanuel Müller, Ira Assent, Uwe Steinhausen:
Outlier detection and ranking based on subspace clustering. Uncertainty Management in Information Systems 2008 - 2007
- [i2]Ira Assent, Ralph Krieger, Emmanuel Müller, Thomas Seidl:
Subspace outlier mining in large multimedia databases. Parallel Universes and Local Patterns 2007 - 2006
- [i1]Ira Assent, Thomas Seidl:
Efficient multi-step query processing for EMD-based similarity. Content-Based Retrieval 2006
Coauthor Index
[j37] [j34] [j29] [j23] [c221] [c219] [c218] [c216] [c214] [c212] [c210] [c209] [c208] [c206] [c205] [c204] [c203] [c202] [c201] [c200] [c199] [c198] [c197] [c195] [c194] [c190] [c188] [c187] [c186] [c185] [c184] [c181] [c176] [c164] [c155] [c132] [c116] [c113] [c110] [c105] [c104] [c103] [c102] [c97] [c96] [c92] [c88] [c82] [c81] [c80] [c78] [c73] [c70] [c68] [c66] [c60] [c52] [e8] [e6]
Evgheniy Faerman
aka: Evgeniy Faerman
aka: Evgeniy Faerman
Sandra Gilhuber
aka: Sandra Obermeier
aka: Sandra Obermeier
Maximilian von Zastrow
aka: Maximilian Archimedes Xaver Hünemörder
aka: Maximilian Hünemörder
aka: Max Hünemörder
aka: Maximilian Archimedes Xaver Hünemörder
aka: Maximilian Hünemörder
aka: Max Hünemörder
Anca Maria Zimmer
aka: Anca Maria Ivanescu
aka: Anca Maria Ivanescu
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