World's Best Scientists 2026 revealed!
Peer Kröger

Peer Kröger

D-Index & Metrics

Computer Science

D-Index
42
Citations
10043
World Ranking
8241
National Ranking
402

Overview

Peer Kröger is affiliated with Kiel University in Germany and has a research focus primarily within the field of Computer Science. Their work spans multiple subfields including Artificial Intelligence, Signal Processing, Computational Theory and Mathematics, Computer Networks and Communications, and Transportation.

The scientist's main research topics include:

  • Anomaly Detection Techniques and Applications
  • Data Management and Algorithms
  • Human Mobility and Location-Based Analysis
  • Advanced Clustering Algorithms Research
  • Advanced Database Systems and Queries
  • Archaeology and ancient environmental studies
  • Vehicular Ad Hoc Networks (VANETs)

Peer Kröger has contributed to numerous publications with presence in prominent venues such as:

  • arXiv (Cornell University)
  • Informatik-Spektrum
  • 2021 International Conference on Data Mining Workshops (ICDMW)
  • ACM Transactions on Spatial Algorithms and Systems
  • Data Mining and Knowledge Discovery

Recent papers authored or co-authored by Kröger include:

  • "Mobility Data Science: Perspectives and Challenges," 2024, ACM Transactions on Spatial Algorithms and Systems
  • "Enhancing cluster analysis via topological manifold learning," 2023, Data Mining and Knowledge Discovery
  • "Multi-isotope fingerprints (O, Sr and Pb) in archaeological animal bone bioapatite: Similarity search and the suitability for provenance analysis in a geologically complex Alpine region," 2020, Archaeometry
  • "Tracking the Evolution of Water Flow Patterns Based on Spatio-Temporal Particle Flow Clusters," 2022, 2022 23rd IEEE International Conference on Mobile Data Management (MDM)
  • "Towards Mobility Data Science (Vision Paper)," 2023, arXiv (Cornell University)

Collaborations are notable with several frequent co-authors who have contributed to multidisciplinary angles within Kröger's research. These include Matthias Renz, Daniyal Kazempour, Maximilian Hünemörder, Andreas Züfle, and Andreas Lohrer.

The research contributions by Peer Kröger emphasize data-driven approaches, algorithmic development, and mobility science, integrating methods from artificial intelligence and data mining to address complex challenges in mobility patterns, clustering, and environmental provenance analysis.

Best Publications

  • Clustering high-dimensional data: A survey on subspace clustering, pattern-based clustering, and correlation clustering

    Hans-Peter Kriegel;Peer Kröger;Arthur Zimek

  • Density-based clustering

    Hans Peter Kriegel;Peer Kröger;Jörg Sander;Arthur Zimek

  • LoOP: local outlier probabilities

    Hans-Peter Kriegel;Peer Kröger;Erich Schubert;Arthur Zimek

  • Density‐based clustering

    Unknown

  • Density-Connected Subspace Clustering for High-Dimensional Data

    Karin Kailing;Hans-Peter Kriegel;Peer Kroger

  • Outlier Detection in Axis-Parallel Subspaces of High Dimensional Data

    Hans-Peter Kriegel;Peer Kröger;Erich Schubert;Arthur Zimek

  • Can shared-neighbor distances defeat the curse of dimensionality?

    Michael E. Houle;Hans-Peter Kriegel;Peer Kröger;Erich Schubert

  • Interpreting and Unifying Outlier Scores

    Hans-Peter Kriegel;Peer Kröger;Erich Schubert;Arthur Zimek

  • Future trends in data mining

    Hans-Peter Kriegel;Karsten M. Borgwardt;Peer Kröger;Alexey Pryakhin

  • Density connected clustering with local subspace preferences

    C. Bohm;K. Railing;H.-P. Kriegel;P. Kroger

  • Computing Clusters of Correlation Connected objects

    Christian Böhm;Karin Kailing;Peer Kröger;Arthur Zimek

  • A generic framework for efficient subspace clustering of high-dimensional data

    H.-P. Kriegel;P. Kroger;M. Renz;S. Wurst

  • Efficient reverse k-nearest neighbor search in arbitrary metric spaces

    Elke Achtert;Christian Böhm;Peer Kröger;Peter Kunath

  • On Using Class-Labels in Evaluation of Clusterings

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

  • Outlier Detection in Arbitrarily Oriented Subspaces

    Hans-Peter Kriegel;Peer Kroger;Erich Schubert;Arthur Zimek

  • DeLi-Clu: boosting robustness, completeness, usability, and efficiency of hierarchical clustering by a closest pair ranking

    Elke Achtert;Christian Böhm;Peer Kröger

  • A General Framework for Increasing the Robustness of PCA-Based Correlation Clustering Algorithms

    Hans-Peter Kriegel;Peer Kröger;Erich Schubert;Arthur Zimek

  • Density-based Projected Clustering over High Dimensional Data Streams.

    Irene Ntoutsi;Arthur Zimek;Themis Palpanas;Peer Kröger

  • Similarity search on time series based on threshold queries

    Johannes Aßfalg;Hans-Peter Kriegel;Peer Kröger;Peter Kunath

  • Data bubbles: quality preserving performance boosting for hierarchical clustering

    Markus M. Breunig;Hans-Peter Kriegel;Peer Kröger;Jörg Sander

  • Subspace clustering

    Hans-Peter Kriegel;Peer Kröger;Arthur Zimek

Frequent Co-Authors

Hans-Peter Kriegel
Hans-Peter Kriegel Ludwig-Maximilians-Universität München
Arthur Zimek
Arthur Zimek University of Southern Denmark
Thomas Seidl
Thomas Seidl Ludwig-Maximilians-Universität München
Christoph Mayr
Christoph Mayr University of Erlangen-Nuremberg
Peter Widmayer
Peter Widmayer ETH Zurich
François Bry
François Bry Ludwig-Maximilians-Universität München
Jörg Sander
Jörg Sander University of Alberta
Craig A. Knoblock
Craig A. Knoblock University of Southern California
Ian Horrocks
Ian Horrocks University of Oxford
Terttu Vartiainen
Terttu Vartiainen Finnish Institute for Health and Welfare (THL)

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