World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
51
Citations
11796
World Ranking
5293
National Ranking
245

Overview

Thomas Seidl is affiliated with Ludwig-Maximilians-Universität München in Germany. Their research contributions span primarily the field of Computer Science, with a focus on subfields such as Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Signal Processing, and Statistical and Nonlinear Physics.

The scientist's publication record includes a notable number of papers in key venues, with a significant presence in arXiv (Cornell University) where they have published 23 works. Other frequent publication venues include the 2021 International Conference on Data Mining Workshops (ICDMW), Proceedings of the AAAI Conference on Artificial Intelligence, Datenbank-Spektrum, and Rock Mechanics and Rock Engineering.

Thomas Seidl's recent papers demonstrate a range of interests and topics. Selected papers include:

  • "Blastability and Ore Grade Assessment from Drill Monitoring for Open Pit Applications" (2021, Rock Mechanics and Rock Engineering)
  • "Argument Mining Driven Analysis of Peer-Reviews" (2021, Proceedings of the AAAI Conference on Artificial Intelligence)
  • "InstanceFormer: An Online Video Instance Segmentation Framework" (2023, Proceedings of the AAAI Conference on Artificial Intelligence)
  • "Over-optimistic evaluation and reporting of novel cluster algorithms: an illustrative study" (2022, Advances in Data Analysis and Classification)
  • "VR Based Visualization and Exploration of Plant Biological Data" (2024, Fraunhofer-Publica (Fraunhofer-Gesellschaft))

The scientist frequently collaborates with other researchers, with their most common coauthors being Matthias Schubert, Anna Beer, Florian Richter, Tanveer Hannan, and Ludwig Zellner. The counts associated with these collaborations range from eight to eleven joint publications per coauthor.

Thomas Seidl's research covers various topics within computer science, including:

  • Anomaly Detection Techniques and Applications
  • Advanced Clustering Algorithms Research
  • Topic Modeling
  • Data Stream Mining Techniques
  • Machine Learning and Algorithms
  • Data Management and Algorithms
  • Software Engineering Research

Best Publications

  • MOA: Massive Online Analysis, a framework for stream classification and clustering.

    Albert Bifet;Geoffrey Holmes;Bernhard Pfahringer;Philipp Kranen

  • 3D Shape Histograms for Similarity Search and Classification in Spatial Databases

    Mihael Ankerst;Gabi Kastenmüller;Hans-Peter Kriegel;Thomas Seidl

  • Optimal multi-step k-nearest neighbor search

    Thomas Seidl;Hans-Peter Kriegel

  • Evaluating Clustering in Subspace Projections of High Dimensional Data

    Emmanuel Müller;Stephan Günnemann;Ira Assent;Thomas Seidl

  • The ClusTree: indexing micro-clusters for anytime stream mining

    Philipp Kranen;Ira Assent;Corinna Baldauf;Thomas Seidl

  • Efficient User-Adaptable Similarity Search in Large Multimedia Databases

    Thomas Seidl;Hans-Peter Kriegel

  • Fast nearest neighbor search in high-dimensional space

    S. Berchtold;B. Ertl;D.A. Keim;H.-P. Kriegel

  • Nearest Neighbor Classification in 3D Protein Databases

    Mihael Ankerst;Gabi Kastenmüller;Hans-Peter Kriegel;Thomas Seidl

  • Managing Intervals Efficiently in Object-Relational Databases

    Hans-Peter Kriegel;Marco Pötke;Thomas Seidl

  • DUSC: Dimensionality Unbiased Subspace Clustering

    I. Assent;R. Krieger;E. Muller;T. Seidl

  • Statistical selection of relevant subspace projections for outlier ranking

    Emmanuel Muller;Matthias Schiffer;Thomas Seidl

  • Mining coherent subgraphs in multi-layer graphs with edge labels

    Brigitte Boden;Stephan Günnemann;Holger Hoffmann;Thomas Seidl

  • On Using Class-Labels in Evaluation of Clusterings

    Ines Färber;Stephan Günnemann;Hans-Peter Kriegel;Peer Kröger

  • Subspace Clustering Meets Dense Subgraph Mining: A Synthesis of Two Paradigms

    Stephan Gunnemann;Ines Farber;Brigitte Boden;Thomas Seidl

  • Clicks: An effective algorithm for mining subspace clusters in categorical datasets

    Mohammed J. Zaki;Markus Peters;Ira Assent;Thomas Seidl

  • Signature Quadratic Form Distance

    Christian Beecks;Merih Seran Uysal;Thomas Seidl

  • Subspace search and visualization to make sense of alternative clusterings in high-dimensional data

    Andrada Tatu;Fabian Maas;Ines Farber;Enrico Bertini

  • INSCY: Indexing Subspace Clusters with In-Process-Removal of Redundancy

    I. Assent;R. Krieger;E. Muller;T. Seidl

  • An effective evaluation measure for clustering on evolving data streams

    Hardy Kremer;Philipp Kranen;Timm Jansen;Thomas Seidl

  • AnyOut: anytime outlier detection on streaming data

    Ira Assent;Philipp Kranen;Corinna Baldauf;Thomas Seidl

Frequent Co-Authors

Ira Assent
Ira Assent Aarhus University
Stephan Günnemann
Stephan Günnemann Technical University of Munich
Hans-Peter Kriegel
Hans-Peter Kriegel Ludwig-Maximilians-Universität München
Peer Kröger
Peer Kröger Kiel University
Daniel A. Keim
Daniel A. Keim University of Konstanz
Rik Van de Walle
Rik Van de Walle Ghent University
Bernhard Pfahringer
Bernhard Pfahringer University of Waikato
Albert Bifet
Albert Bifet University of Waikato
Volker Tresp
Volker Tresp Ludwig-Maximilians-Universität München
Nikos Mamoulis
Nikos Mamoulis University of Ioannina

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