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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)
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2020 – today
- 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)- [c265]Florian Richter, Janina Sontheim, Ludwig Zellner, Thomas Seidl:
TADE: Stochastic Conformance Checking Using Temporal Activity Density Estimation. BPM 2020: 220-236 - [c264]Florian Richter, Yifeng Lu, Daniyal Kazempour, Thomas Seidl:
AMTICS: Aligning Micro-clusters to Identify Cluster Structures. DASFAA (1) 2020: 752-768 - [c263]Julian Busch, Jiaxing Pi, Thomas Seidl:
PushNet: Efficient and Adaptive Neural Message Passing. ECAI 2020: 1039-1046 - [c262]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 - [c261]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 - [c260]Anna Beer, Valentin Hartmann, Thomas Seidl:
Orderings of Data - More Than a Tripping Hazard: Visionary. SSDBM 2020: 17:1-17:4 - [p5]Yifeng Lu, Florian Richter, Thomas Seidl:
Efficient Infrequent Pattern Mining Using Negative Itemset Tree. Complex Pattern Mining 2020: 1-16 - [i8]Julian Busch, Jiaxing Pi, Thomas Seidl:
PushNet: Efficient and Adaptive Neural Message Passing. CoRR abs/2003.02228 (2020)
2010 – 2019
- 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) - [c259]Florian Richter, Ludwig Zellner, Imen Azaiz, David Winkel, Thomas Seidl:
LIProMa: Label-Independent Process Matching. Business Process Management Workshops 2019: 186-198 - [c258]Janis Held, Anna Beer, Thomas Seidl:
Chain-detection for DBSCAN. BTW (Workshops) 2019: 173-183 - [c257]Daniyal Kazempour, Maksim Kazakov, Peer Kröger, Thomas Seidl:
DICE: Density-based Interactive Clustering and Exploration. BTW 2019: 547-550 - [c256]Anna Beer, Daniyal Kazempour, Thomas Seidl:
Rock - Let the points roam to their clusters themselves. EDBT 2019: 630-633 - [c255]Daniyal Kazempour, Lisa Krombholz, Peer Kröger, Thomas Seidl:
A Galaxy of Correlations. EDBT 2019: 702-705 - [c254]Daniyal Kazempour, Thomas Seidl:
Insights into a running clockwork: On interactive process-aware clustering. EDBT 2019: 706-709 - [c253]Anna Beer, Daniyal Kazempour, Marcel Baur, Thomas Seidl:
Human Learning in Data Science. HCI (34) 2019: 170-176 - [c252]Daniyal Kazempour, Anna Beer, Thomas Seidl:
Data on RAILs: On Interactive Generation of Artificial Linear Correlated Data. HCI (34) 2019: 184-189 - [c251]Anna Beer, Nadine Sarah Schüler, Thomas Seidl:
A Generator for Subspace Clusters. LWDA 2019: 69-73 - [c250]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 - [c249]Daniyal Kazempour, Anna Beer, Oliver Schrüfer, Thomas Seidl:
Clustering Trend Data Time-Series through Segmentation of FFT-decomposed Signal Constituents. LWDA 2019: 127-138 - [c248]Daniyal Kazempour, Long Mathias Yan, Thomas Seidl:
From Covariance to Comode in context of Principal Component Analysis. LWDA 2019: 139-143 - [c247]Florian Richter, Florian Wahl, Alona Sydorova, Thomas Seidl:
k-process: Model-Conformance-based Clustering of Process Instances. LWDA 2019: 161-172 - [c246]Janina Sontheim, Florian Richter, Thomas Seidl:
Temporal Deviations on Event Sequences. LWDA 2019: 173-177 - [c245]Florian Richter, Ludwig Zellner, Janina Sontheim, Thomas Seidl:
Model-Aware Clustering of Non-conforming Traces. OTM Conferences 2019: 193-200 - [c244]Anna Beer, Jennifer Lauterbach, Thomas Seidl:
MORe++: k-Means Based Outlier Removal on High-Dimensional Data. SISAP 2019: 188-202 - [c243]Maximilian Archimedes Xaver Hünemörder, Daniyal Kazempour, Peer Kröger, Thomas Seidl:
SIDEKICK: Linear Correlation Clustering with Supervised Background Knowledge. SISAP 2019: 221-230 - [c242]Daniyal Kazempour, M. A. X. Hünemörder, Thomas Seidl:
On coMADs and Principal Component Analysis. SISAP 2019: 273-280 - [c241]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 - [c240]Anna Beer, Thomas Seidl:
Graph Ordering and Clustering: A Circular Approach. SSDBM 2019: 185-188 - [c239]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 - [c238]Daniyal Kazempour, Thomas Seidl:
On systematic hyperparameter analysis through the example of subspace clustering. SSDBM 2019: 226-229 - [c237]Michael Fromm, Evgeniy Faerman, Thomas Seidl:
TACAM: Topic And Context Aware Argument Mining. WI 2019: 99-106 - [c236]Yifeng Lu, Florian Richter, Thomas Seidl:
LSCMiner: Efficient Low Support Closed Itemsets Mining. WISE 2019: 293-309 - [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) - 2018
- [j38]Göran Kauermann, Thomas Seidl:
Data Science: a proposal for a curriculum. Int. J. Data Sci. Anal. 6(3): 195-199 (2018) - [c235]Yifeng Lu, Florian Richter, Thomas Seidl:
Efficient Infrequent Itemset Mining Using Depth-First and Top-Down Lattice Traversal. DASFAA (1) 2018: 908-915 - [c234]Yifeng Lu, Thomas Seidl:
Towards Efficient Closed Infrequent Itemset Mining Using Bi-Directional Traversing. DSAA 2018: 140-149 - [c233]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 - [c232]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 - [c231]Daniyal Kazempour, Thomas Seidl:
Identifying Entangled Data Points on Iteration Trajectories of Clusterings. LWDA 2018: 174-178 - [c230]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 - [c229]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 - [r2]Thomas Seidl:
Nearest Neighbor Classification. Encyclopedia of Database Systems (2nd ed.) 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) - [c228]Florian Richter, Thomas Seidl:
TESSERACT: Time-Drifts in Event Streams Using Series of Evolving Rolling Averages of Completion Times. BPM 2017: 289-305 - [c227]Thomas Seidl:
Multimedia Similarity Search. BTW (Workshops) 2017: 397 - [c226]Marwan Hassani, Daniel Töws, Thomas Seidl:
Understanding the bigger picture: batch-free exploration of streaming sequential patterns with accurate prediction. SAC 2017: 866-869 - [c225]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 - [c224]Yifeng Lu, Marwan Hassani, Thomas Seidl:
Incremental Temporal Pattern Mining Using Efficient Batch-Free Stream Clustering. SSDBM 2017: 7:1-7:12 - [c223]Daniyal Kazempour, Markus Mauder, Peer Kröger, Thomas Seidl:
Detecting Global Hyperparaboloid Correlated Clusters Based on Hough Transform. SSDBM 2017: 31:1-31:6 - [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] - 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) - [c222]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 - [c221]Merih Seran Uysal, Daniel Sabinasz, Thomas Seidl:
Approximation-Based Efficient Query Processing with the Earth Mover's Distance. DASFAA (2) 2016: 165-180 - [c220]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 - [c219]Marwan Hassani, Yifeng Lu, Thomas Seidl:
Towards an Efficient Ranking of Interval-Based Patterns. EDBT 2016: 688-689 - [c218]Christian Beecks, Merih Seran Uysal, Thomas Seidl:
Distance-based Multimedia Indexing. EDBT 2016: 722-723 - [c217]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 - [c216]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 - [c215]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 - [c214]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 - [c213]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 - [c212]Roland Assam, Subramanyam Sathyanarayana, Thomas Seidl:
Infusing Geo-Recency Mixture Models for Effective Location Prediction in LBSN. SDM 2016: 855-863 - [c211]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
- [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) - [c210]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 - [c209]Christian Beecks, Merih Seran Uysal, Thomas Seidl:
Gradient-based signatures for big multimedia data. BigData 2015: 2834-2835 - [c208]Daniel Töws, Marwan Hassani, Christian Beecks, Thomas Seidl:
Optimizing Sequential Pattern Mining Within Multiple Streams. BTW Workshops 2015: 223-232 - [c207]Christian Beecks, Merih Seran Uysal, Thomas Seidl:
Distance-based Multimedia Indexing. BTW Workshops 2015: 265-268 - [c206]Volker Markl, Erhard Rahm, Wolfgang Lehner, Michael Beigl, Thomas Seidl:
Big Data-Zentren - Vorstellung und Panel. BTW 2015: 477-479 - [c205]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 - [c204]Merih Seran Uysal, Christian Beecks, Thomas Seidl:
On efficient content-based near-duplicate video detection. CBMI 2015: 1-6 - [c203]Merih Seran Uysal, Christian Beecks, Daniel Sabinasz, Thomas Seidl:
Large-scale Efficient and Effective Video Similarity Search. LSDS-IR@CIKM 2015: 3-8 - [c202]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 - [c201]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 - [c200]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 - [c199]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 - [c198]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 - [c197]Christian Beecks, Merih Seran Uysal, Thomas Seidl:
Earth Mover's Distance vs. Quadratic form Distance: An Analytical and Empirical Comparison. ISM 2015: 233-236 - [c196]Merih Seran Uysal, Christian Beecks, Daniel Sabinasz, Thomas Seidl:
Effective Content-Based Near-Duplicate Video Detection. ISM 2015: 254-257 - [c195]Marwan Hassani, Pascal Spaus, Alfredo Cuzzocrea, Thomas Seidl:
Adaptive Stream Clustering Using Incremental Graph Maintenance. BigMine 2015: 49-64 - [c194]Marwan Hassani, Christian Beecks, Daniel Töws, Thomas Seidl:
Mining Sequential Patterns of Event Streams in a Smart Home Application. LWA 2015: 159-170 - [c193]Christian Beecks, Merih Seran Uysal, Thomas Seidl:
Content-Based Image Retrieval with Gaussian Mixture Models. MMM (1) 2015: 294-305 - [c192]Marwan Hassani, Thomas Seidl:
Internal Clustering Evaluation of Data Streams. PAKDD Workshops 2015: 198-209 - [c191]Roland Assam, Simon Feiden, Thomas Seidl:
UrbanHubble: Location Prediction and Geo-Social Analytics in LBSN. ECML/PKDD (3) 2015: 329-332 - [c190]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 - [c189]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 - [c188]Marwan Hassani, Sergio Siccha, Florian Richter, Thomas Seidl:
Efficient Process Discovery From Event Streams Using Sequential Pattern Mining. SSCI 2015: 1366-1373 - [c187]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 - [c186]Merih Seran Uysal, Christian Beecks, Jochen Schmücking, Thomas Seidl:
Efficient similarity search in scientific databases with feature signatures. SSDBM 2015: 30:1-30:12 - [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
- [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) - [c185]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 - [c184]Merih Seran Uysal, Christian Beecks, Thomas Seidl:
On Efficient Query Processing with the Earth Mover's Distance. PIKM@CIKM 2014: 25-32 - [c183]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 - [c182]Marwan Hassani, Thomas Seidl:
Efficient Streaming Detection of Hidden Clusters in Big Data Using Subspace Stream Clustering. DASFAA Workshops 2014: 146-160 - [c181]Sergej Fries, Stephan Wels, Thomas Seidl:
Projected Clustering for Huge Data Sets in MapReduce. EDBT 2014: 49-60 - [c180]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 - [c179]Roland Assam, Thomas Seidl:
Prediction of freezing of gait from Parkinson's Disease movement time series using conditional random fields. HealthGIS 2014: 11-20 - [c178]Sergej Fries, Brigitte Boden, Grzegorz Stepien, Thomas Seidl:
PHiDJ: Parallel similarity self-join for high-dimensional vector data with MapReduce. ICDE 2014: 796-807 - [c177]Roland Assam, Thomas Seidl:
Check-in Location Prediction Using Wavelets and Conditional Random Fields. ICDM 2014: 713-718 - [c176]Stephan Günnemann, Ines Färber, Matthias Rüdiger, Thomas Seidl:
SMVC: semi-supervised multi-view clustering in subspace projections. KDD 2014: 253-262 - [c175]Christian Beecks, Steffen Kirchhoff, Thomas Seidl:
On the Stability of Signature-Based Distance Functions for Content-Based Image Retrieval. LWA 2014: 226 - [c174]Marwan Hassani, Pascal Spaus, Thomas Seidl:
Adaptive Multiple-Resolution Stream Clustering. MLDM 2014: 134-148 - [c173]Roland Assam, Thomas Seidl:
Effective Map Matching Using Curve Tangents and Hidden Markov Model. MSN 2014: 213-219 - [c172]Roland Assam, Thomas Seidl:
Context-based location clustering and prediction using conditional random fields. MUM 2014: 1-10 - [c171]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 - [c170]Brigitte Boden, Martin Ester, Thomas Seidl:
Density-Based Subspace Clustering in Heterogeneous Networks. ECML/PKDD (1) 2014: 149-164 - [c169]Roland Assam, Marwan Hassani, Michael Brysch, Thomas Seidl:
(k, d)-core anonymity: structural anonymization of massive networks. SSDBM 2014: 17:1-17:12 - [c168]Anca Maria Zimmer, Philip Driessen, Philipp Kranen, Thomas Seidl:
Inverse predictions on continuous models in scientific databases. SSDBM 2014: 26:1-26:12 - [c167]Marwan Hassani, Philipp Kranen, Rajveer Saini, Thomas Seidl:
Subspace anytime stream clustering. SSDBM 2014: 37:1-37:4 - [c166]Roland Assam, Thomas Seidl:
Near-Optimal Activity Prediction through Efficient Wavelet Modulus Maxima Partitioning and Conditional Random Fields. UIC/ATC/ScalCom 2014: 236-243 - [c165]Roland Assam, Thomas Seidl:
Activity recognition from sensors using dyadic wavelets and Hidden Markov Model. WiMob 2014: 442-448 - [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 - [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] - [i5]Stephan Günnemann, Hardy Kremer, Matthias Hannen, Thomas Seidl:
KDD-SC: Subspace Clustering Extensions for Knowledge Discovery Frameworks. CoRR abs/1407.3850 (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) - [c164]Thomas Seidl, Sergej Fries, Brigitte Boden:
MR-DSJ: Distance-Based Self-Join for Large-Scale Vector Data Analysis with MapReduce. BTW 2013: 37-56 - [c163]Christian Beecks, Merih Seran Uysal, Philip Driessen, Thomas Seidl:
Content-based exploration of multimedia databases. CBMI 2013: 59-64