Peer Kröger

7.1k total citations · 2 hit papers
99 papers, 3.6k citations indexed

About

Peer Kröger is a scholar working on Artificial Intelligence, Signal Processing and Computer Vision and Pattern Recognition. According to data from OpenAlex, Peer Kröger has authored 99 papers receiving a total of 3.6k indexed citations (citations by other indexed papers that have themselves been cited), including 50 papers in Artificial Intelligence, 48 papers in Signal Processing and 27 papers in Computer Vision and Pattern Recognition. Recurrent topics in Peer Kröger's work include Data Management and Algorithms (47 papers), Advanced Clustering Algorithms Research (28 papers) and Data Mining Algorithms and Applications (12 papers). Peer Kröger is often cited by papers focused on Data Management and Algorithms (47 papers), Advanced Clustering Algorithms Research (28 papers) and Data Mining Algorithms and Applications (12 papers). Peer Kröger collaborates with scholars based in Germany, Canada and Denmark. Peer Kröger's co-authors include Hans‐Peter Kriegel, Arthur Zimek, Jörg Sander, Erich Schubert, Karin Kailing, Christian Böhm, Matthias Renz, H.-P. Kriegel, Elke Achtert and Alexey Pryakhin and has published in prestigious journals such as The Science of The Total Environment, Palaeogeography Palaeoclimatology Palaeoecology and Machine Learning.

In The Last Decade

Peer Kröger

89 papers receiving 3.4k citations

Hit Papers

align trajectories sort by overperformance

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Clustering high-dimensional data

2009 732 citations Hans‐Peter Kriegel, Peer Kröger et al. profile →
Density‐based clustering

2011 554 citations Hans‐Peter Kriegel, Peer Kröger et al. Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery profile →

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Peer Kröger	2.2k	1.1k	959	620	493	99	3.6k
David Arthur	1.8k 0.8×	686 0.6×	1.2k 1.3×	413 0.7×	397 0.8×	16	4.4k
Ricardo J. G. B. Campello	2.4k 1.1×	639 0.6×	591 0.6×	373 0.6×	432 0.9×	67	3.4k
Pasi Fränti	2.7k 1.2×	1.4k 1.2×	1.7k 1.8×	469 0.8×	315 0.6×	228	4.7k
Mihael Ankerst	2.2k 1.0×	1.5k 1.3×	1.3k 1.4×	745 1.2×	369 0.7×	15	4.2k
Jing Gao	4.0k 1.8×	788 0.7×	1.1k 1.1×	1.2k 1.9×	648 1.3×	164	6.4k
Rui Xu	2.5k 1.1×	699 0.6×	2.0k 2.1×	650 1.0×	412 0.8×	77	5.4k
Tobias Scheffer	2.0k 0.9×	402 0.4×	877 0.9×	532 0.9×	289 0.6×	104	3.5k
Christine Piatko	2.1k 0.9×	683 0.6×	2.1k 2.2×	602 1.0×	531 1.1×	57	5.8k
Nathan Srebro	3.4k 1.5×	623 0.5×	2.1k 2.2×	887 1.4×	450 0.9×	83	5.7k
Fu-Lai Chung	3.6k 1.6×	901 0.8×	2.1k 2.2×	588 0.9×	330 0.7×	248	6.1k

Countries citing papers authored by Peer Kröger

Since Specialization

Citations

This map shows the geographic impact of Peer Kröger's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Peer Kröger with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Peer Kröger more than expected).

Fields of papers citing papers by Peer Kröger

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Peer Kröger. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Peer Kröger. The network helps show where Peer Kröger may publish in the future.

Co-authorship network of co-authors of Peer Kröger

This figure shows the co-authorship network connecting the top 25 collaborators of Peer Kröger. A scholar is included among the top collaborators of Peer Kröger based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Peer Kröger. Peer Kröger is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Liewald, Mathias, Birgit Vogel‐Heuser, Thomas Bergs, Marco F. Huber, & Peer Kröger. (2025). Advancing data-driven process modeling in metal forming. at - Automatisierungstechnik. 73(3). 162–173. 1 indexed citations

Kazempour, Daniyal, et al.. (2024). CoMadOut—a robust outlier detection algorithm based on CoMAD. Machine Learning. 113(10). 8061–8135. 2 indexed citations

Kazempour, Daniyal, et al.. (2024). Do good scores imply good embeddings? On the necessity of inspecting manifold learning evaluation results by multiple criteria. 317–324.

Kröger, Peer, et al.. (2024). GADformer: A Transparent Transformer Model for Group Anomaly Detection on Trajectories. 1–8. 2 indexed citations

Kazempour, Daniyal, et al.. (2023). Interactive Detection and Visualization of Ocean Carbon Regimes. Helmholtz Centre for Ocean Research Kiel (GEOMAR). 171–174.

Kröger, Peer, et al.. (2023). Enhancing AIS Vessel Trajectories via Trip Detection. 11. 1–2.

Kazempour, Daniyal, et al.. (2023). Detection and Tracking of Dynamic Ocean Carbon Uptake Regimes Built Upon Spatial Target-Driver Relationships via Adaptive Hierarchical Clustering. Helmholtz Centre for Ocean Research Kiel (GEOMAR). 113. 1–10.

Herrmann, Moritz, Daniyal Kazempour, Fabian Scheipl, & Peer Kröger. (2023). Enhancing cluster analysis via topological manifold learning. Data Mining and Knowledge Discovery. 38(3). 840–887. 5 indexed citations

Kröger, Peer, et al.. (2022). Layer-Wise Relevance Propagation for Echo State Networks Applied to Earth System Variability. Helmholtz Centre for Ocean Research Kiel (GEOMAR). 3 indexed citations

10.

Grupe, Gisela, et al.. (2020). Provenance analysis of cremated skeletal remains by stable isotopes. Anthropologischer Anzeiger. 78(1-2). 21–32.

11.

Kröger, Peer, et al.. (2019). Evidence for sea spray effect on oxygen stable isotopes in bone phosphate — Approximation and correction using Gaussian Mixture Model clustering. The Science of The Total Environment. 673. 668–684. 4 indexed citations

12.

Emrich, Tobias, et al.. (2014). On reverse-k-nearest-neighbor joins. GeoInformatica. 19(2). 299–330. 6 indexed citations

13.

Emrich, Tobias, et al.. (2013). A Mutual Pruning Approach for RkNN Join Processing.. BTW. 21–35. 1 indexed citations

14.

Kriegel, Hans‐Peter, Peer Kröger, Erich Schubert, & Arthur Zimek. (2011). Interpreting and Unifying Outlier Scores. 13–24. 166 indexed citations

15.

Kriegel, Hans‐Peter, Peer Kröger, Jörg Sander, & Arthur Zimek. (2011). Density‐based clustering. Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery. 1(3). 231–240. 554 indexed citations breakdown →

16.

Kriegel, Hans‐Peter, Peer Kröger, Irene Ntoutsi, & Arthur Zimek. (2010). Towards subspace clustering on dynamic data. 31–38. 4 indexed citations

17.

Kriegel, Hans‐Peter, Peer Kröger, Peter Kunath, & Matthias Renz. (2007). Generalizing the Optimality of Multi-Step k-Nearest Neighbor Query Processing. 12 indexed citations

18.

Kriegel, H.-P., et al.. (2006). Efficient Query Processing in Arbitrary Subspaces Using Vector Approximations. 184–190. 10 indexed citations

19.

Achtert, Elke, Christian Böhm, Peer Kröger, & Arthur Zimek. (2006). Mining Hierarchies of Correlation Clusters. 119–128. 22 indexed citations

20.

Kailing, Karin, Hans‐Peter Kriegel, & Peer Kröger. (2004). Density-Connected Subspace Clustering for High-Dimensional Data. 246–256. 225 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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