Thomas Seidl
Thomas Seidl
Professor of Computer Science, Ludwig-Maximilians-Universität München (LMU Munich), Germany
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Zitiert von
Zitiert von
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
JMLR Proceedings of Machine Learning Research 11 (WAPA), 44-50, 2010
3D shape histograms for similarity search and classification in spatial databases
M Ankerst, G Kastenmüller, HP Kriegel, T Seidl
International symposium on spatial databases, 207-226, 1999
Optimal multi-step k-nearest neighbor search
T Seidl, HP Kriegel
Proceedings of the 1998 ACM SIGMOD international conference on Management of …, 1998
Evaluating clustering in subspace projections of high dimensional data
E Müller, S Günnemann, I Assent, T Seidl
Proceedings of the VLDB Endowment 2 (1), 1270-1281, 2009
The ClusTree: indexing micro-clusters for anytime stream mining
P Kranen, I Assent, C Baldauf, T Seidl
Knowledge and information systems 29 (2), 249-272, 2011
Efficient user-adaptable similarity search in large multimedia databases
T Seidl, HP Kriegel
VLDB 97, 506-515, 1997
Fast nearest neighbor search in high-dimensional space
S Berchtold, B Ertl, DA Keim, HP Kriegel, T Seidl
Proceedings 14th International Conference on Data Engineering, 209-218, 1998
Nearest neighbor classification in 3D protein databases.
M Ankerst, G Kastenmüller, HP Kriegel, T Seidl
ISMB 99, 34-43, 1999
Managing intervals efficiently in object-relational databases
HP Kriegel, M Pötke, T Seidl
VLDB 20 (0), 0, 2000
DUSC: Dimensionality unbiased subspace clustering
I Assent, R Krieger, E Müller, T Seidl
seventh IEEE international conference on data mining (ICDM 2007), 409-414, 2007
Statistical selection of relevant subspace projections for outlier ranking
E Müller, M Schiffer, T Seidl
2011 IEEE 27th international conference on data engineering, 434-445, 2011
On using class-labels in evaluation of clusterings
I Färber, S Günnemann, HP Kriegel, P Kröger, E Müller, E Schubert, ...
MultiClust: 1st international workshop on discovering, summarizing and using …, 2010
Clicks: An effective algorithm for mining subspace clusters in categorical datasets
MJ Zaki, M Peters, I Assent, T Seidl
Data & Knowledge Engineering 60 (1), 51-70, 2007
Subspace clustering meets dense subgraph mining: A synthesis of two paradigms
S Günnemann, I Färber, B Boden, T Seidl
2010 IEEE international conference on data mining, 845-850, 2010
Signature quadratic form distance
C Beecks, MS Uysal, T Seidl
Proceedings of the ACM International Conference on Image and Video Retrieval …, 2010
Data provenance: A Categorization of existing approaches
B Glavic, KR Dittrich, A Kemper, H Schöning, T Rose, M Jarke, T Seidl, ...
BTW'07: Datenbanksysteme in Buisness, Technologie und Web, 227-241, 2007
INSCY: Indexing subspace clusters with in-process-removal of redundancy
I Assent, R Krieger, E Müller, T Seidl
2008 Eighth IEEE International Conference on Data Mining, 719-724, 2008
Mining coherent subgraphs in multi-layer graphs with edge labels
B Boden, S Günnemann, H Hoffmann, T Seidl
Proceedings of the 18th ACM SIGKDD international conference on Knowledge …, 2012
Subspace search and visualization to make sense of alternative clusterings in high-dimensional data
A Tatu, F Maaß, I Färber, E Bertini, T Schreck, T Seidl, D Keim
2012 IEEE Conference on Visual Analytics Science and Technology (VAST), 63-72, 2012
Efficient similarity search for hierarchical data in large databases
K Kailing, HP Kriegel, S Schönauer, T Seidl
International Conference on Extending Database Technology, 676-693, 2004
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