The scientist’s investigation covers issues in Data mining, Cluster analysis, Artificial intelligence, Pattern recognition and Nearest neighbor search. His Data mining study incorporates themes from Linear subspace, Database, Constrained clustering, Dimensionality reduction and Search engine indexing. His studies deal with areas such as Subspace topology and Data stream mining as well as Cluster analysis.
As a part of the same scientific family, Thomas Seidl mostly works in the field of Artificial intelligence, focusing on Machine learning and, on occasion, Field and Data set. Thomas Seidl combines subjects such as Ranking, Histogram, Quadratic form and Categorical variable with his study of Pattern recognition. The study incorporates disciplines such as Similarity, Similitude, Filter and Series in addition to Nearest neighbor search.
Data mining, Artificial intelligence, Cluster analysis, Pattern recognition and Nearest neighbor search are his primary areas of study. His research investigates the connection between Data mining and topics such as Database that intersect with problems in Information retrieval. His Artificial intelligence research integrates issues from Machine learning and Computer vision.
His works in Correlation clustering, CURE data clustering algorithm, Clustering high-dimensional data, Data stream clustering and Fuzzy clustering are all subjects of inquiry into Cluster analysis. His Pattern recognition research includes themes of Curse of dimensionality and Image retrieval. His biological study spans a wide range of topics, including Filter, Earth mover's distance, Similarity, Algorithm and Similitude.
Thomas Seidl mainly investigates Cluster analysis, Artificial intelligence, Data mining, Pattern recognition and Algorithm. Thomas Seidl interconnects Subspace topology, Point and Measure in the investigation of issues within Cluster analysis. His work deals with themes such as Correlation clustering and Linear subspace, which intersect with Subspace topology.
The Artificial intelligence study combines topics in areas such as Machine learning and Scalability. Thomas Seidl has included themes like Ground truth and Unsupervised learning in his Data mining study. His study in the field of Principal component analysis also crosses realms of Codec.
His primary scientific interests are in Artificial intelligence, Cluster analysis, Knowledge graph, Data mining and Pattern recognition. His Ranking and Visualization study in the realm of Artificial intelligence connects with subjects such as Event, Trace and Anomaly. His work on DBSCAN as part of general Cluster analysis research is often related to Clockwork, thus linking different fields of science.
While the research belongs to areas of Knowledge graph, Thomas Seidl spends his time largely on the problem of Machine learning, intersecting his research to questions surrounding Graph. His Data mining research includes elements of Database transaction, Associative property and Pruning. His study in Pattern recognition is interdisciplinary in nature, drawing from both Matrix and Outlier.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
MOA: Massive Online Analysis, a framework for stream classification and clustering.
Albert Bifet;Geoffrey Holmes;Bernhard Pfahringer;Philipp Kranen.
Proceedings of the First Workshop on Applications of Pattern Analysis (2010)
3D Shape Histograms for Similarity Search and Classification in Spatial Databases
Mihael Ankerst;Gabi Kastenmüller;Hans-Peter Kriegel;Thomas Seidl.
Lecture Notes in Computer Science (1999)
Optimal multi-step k-nearest neighbor search
Thomas Seidl;Hans-Peter Kriegel.
international conference on management of data (1998)
Evaluating clustering in subspace projections of high dimensional data
Emmanuel Müller;Stephan Günnemann;Ira Assent;Thomas Seidl.
very large data bases (2009)
The ClusTree: indexing micro-clusters for anytime stream mining
Philipp Kranen;Ira Assent;Corinna Baldauf;Thomas Seidl.
Knowledge and Information Systems (2011)
Efficient User-Adaptable Similarity Search in Large Multimedia Databases
Thomas Seidl;Hans-Peter Kriegel.
very large data bases (1997)
Fast nearest neighbor search in high-dimensional space
S. Berchtold;B. Ertl;D.A. Keim;H.-P. Kriegel.
international conference on data engineering (1998)
Nearest Neighbor Classification in 3D Protein Databases
Mihael Ankerst;Gabi Kastenmüller;Hans-Peter Kriegel;Thomas Seidl.
intelligent systems in molecular biology (1999)
Managing Intervals Efficiently in Object-Relational Databases
Hans-Peter Kriegel;Marco Pötke;Thomas Seidl.
very large data bases (2000)
DUSC: Dimensionality Unbiased Subspace Clustering
I. Assent;R. Krieger;E. Muller;T. Seidl.
international conference on data mining (2007)
Profile was last updated on December 6th, 2021.
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