Jörg Sander

48.5k total citations · 11 hit papers
97 papers, 29.4k citations indexed

About

Jörg Sander is a scholar working on Artificial Intelligence, Signal Processing and Information Systems. According to data from OpenAlex, Jörg Sander has authored 97 papers receiving a total of 29.4k indexed citations (citations by other indexed papers that have themselves been cited), including 60 papers in Artificial Intelligence, 41 papers in Signal Processing and 23 papers in Information Systems. Recurrent topics in Jörg Sander's work include Data Management and Algorithms (38 papers), Advanced Clustering Algorithms Research (33 papers) and Data Mining Algorithms and Applications (20 papers). Jörg Sander is often cited by papers focused on Data Management and Algorithms (38 papers), Advanced Clustering Algorithms Research (33 papers) and Data Mining Algorithms and Applications (20 papers). Jörg Sander collaborates with scholars based in Canada, Germany and Brazil. Jörg Sander's co-authors include Hans‐Peter Kriegel, Martin Ester, Xiaowei Xu, Markus Breunig, Raymond T. Ng, Mihael Ankerst, Arthur Zimek, Ricardo J. G. B. Campello, Erich Schubert and Peer Kröger and has published in prestigious journals such as European Journal of Operational Research, Hypertension and IEEE Transactions on Knowledge and Data Engineering.

In The Last Decade

Jörg Sander

95 papers receiving 27.8k citations

Hit Papers

A density-based algorithm... 1996 2026 2006 2016 1996 2000 1999 2000 2017 4.0k 8.0k 12.0k

Author Peers

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

Author Last Decade Papers Cites
Jörg Sander 15.1k 7.3k 5.6k 4.6k 3.7k 97 29.4k
Hans‐Peter Kriegel 19.8k 1.3× 13.1k 1.8× 8.9k 1.6× 7.4k 1.6× 5.9k 1.6× 218 39.3k
Martin Ester 10.6k 0.7× 4.9k 0.7× 4.8k 0.9× 2.8k 0.6× 6.4k 1.7× 175 26.7k
Charų C. Aggarwal 14.3k 0.9× 4.8k 0.7× 3.7k 0.7× 4.9k 1.1× 5.8k 1.6× 363 23.7k
Hui Xiong 10.6k 0.7× 3.8k 0.5× 3.7k 0.7× 2.3k 0.5× 6.0k 1.6× 624 26.5k
Vipin Kumar 14.0k 0.9× 4.7k 0.6× 2.7k 0.5× 5.5k 1.2× 4.9k 1.3× 304 25.9k
Peter J. Rousseeuw 11.0k 0.7× 3.7k 0.5× 6.2k 1.1× 1.9k 0.4× 2.4k 0.6× 195 52.8k
Xiaowei Xu 7.3k 0.5× 4.0k 0.5× 3.8k 0.7× 1.8k 0.4× 2.8k 0.7× 56 17.5k
Nitesh V. Chawla 18.1k 1.2× 2.3k 0.3× 3.0k 0.5× 2.8k 0.6× 4.8k 1.3× 320 33.5k
Alex Smola 17.1k 1.1× 3.3k 0.5× 8.9k 1.6× 2.6k 0.6× 2.5k 0.7× 121 37.5k
James C. Bezdek 21.1k 1.4× 5.3k 0.7× 12.6k 2.3× 2.0k 0.4× 3.3k 0.9× 309 39.0k

Countries citing papers authored by Jörg Sander

Since Specialization
Citations

This map shows the geographic impact of Jörg Sander'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 Jörg Sander with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jörg Sander more than expected).

Fields of papers citing papers by Jörg Sander

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jörg Sander. 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 Jörg Sander. The network helps show where Jörg Sander may publish in the future.

Co-authorship network of co-authors of Jörg Sander

This figure shows the co-authorship network connecting the top 25 collaborators of Jörg Sander. A scholar is included among the top collaborators of Jörg Sander 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 Jörg Sander. Jörg Sander is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Sander, Jörg, et al.. (2023). Potential of dissimilarity measure-based computation of protein thermal stability data for determining protein interactions. Briefings in Bioinformatics. 24(3). 1 indexed citations
2.
Sander, Jörg, et al.. (2023). On the evaluation of outlier detection and one-class classification: a comparative study of algorithms, model selection, and ensembles. Data Mining and Knowledge Discovery. 37(4). 1473–1517. 13 indexed citations
3.
Schubert, Erich, et al.. (2017). DBSCAN Revisited, Revisited. ACM Transactions on Database Systems. 42(3). 1–21. 1546 indexed citations breakdown →
4.
Moulavi, Davoud, Pablo Andretta Jaskowiak, Ricardo J. G. B. Campello, Arthur Zimek, & Jörg Sander. (2014). Density-Based Clustering Validation. 839–847. 152 indexed citations
5.
Moulavi, Davoud, et al.. (2012). Combining gene expression and interaction network data to improve kidney lesion score prediction. International Journal of Bioinformatics Research and Applications. 8(1/2). 54–54. 5 indexed citations
6.
Campello, Ricardo J. G. B., Davoud Moulavi, & Jörg Sander. (2012). A Simpler and More Accurate AUTO-HDS Framework for Clustering and Visualization of Biological Data. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 9(6). 1850–1852. 4 indexed citations
7.
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 →
8.
Fokaefs, Marios, Nikolaos Tsantalis, Alexander Chatzigeorgiou, & Jörg Sander. (2009). Decomposing object-oriented class modules using an agglomerative clustering technique. PolyPublie (École Polytechnique de Montréal). 93–101. 47 indexed citations
9.
Lelis, Levi H. S. & Jörg Sander. (2009). Semi-supervised Density-Based Clustering. 842–847. 58 indexed citations
10.
Zhou, Jianjun, Jörg Sander, & Guohui Lin. (2006). Efficient composite pattern finding from monad patterns. International Journal of Bioinformatics Research and Applications. 3(1). 86–86. 3 indexed citations
11.
Sander, Jörg, et al.. (2005). A trajectory splitting model for efficient spatio-temporal indexing. Very Large Data Bases. 934–945. 40 indexed citations
12.
Ng, Raymond T., Jörg Sander, & Monica C. Sleumer. (2001). Hierarchical Cluster analysis of SAGE data for cancer profiling. 65–72. 16 indexed citations
13.
Ester, Martin & Jörg Sander. (2000). Knowledge Discovery in Databases - Techniken und Anwendungen. Springer eBooks. 20 indexed citations
14.
Ester, Martin, Hans‐Peter Kriegel, Jörg Sander, Michael Wimmer, & Xiaowei Xu. (1998). Incremental Clustering for Mining in a Data Warehousing Environment. Very Large Data Bases. 323–333. 296 indexed citations
15.
Ester, Martin, Hans‐Peter Kriegel, Jörg Sander, & Xiaowei Xu. (1998). Clustering for Mining in Large Spatial Databases.. Künstliche Intell.. 12. 18–24. 39 indexed citations
16.
Ester, Martin, et al.. (1998). Algorithms for characterization and trend detection in spatial databases. Knowledge Discovery and Data Mining. 44–50. 69 indexed citations
17.
Ester, Martin, Hans‐Peter Kriegel, Jörg Sander, & Xiaowei Xu. (1997). Density-connected sets and their application for trend detection in spatial databases. Knowledge Discovery and Data Mining. 10–15. 28 indexed citations
18.
Ester, Martin, Hans‐Peter Kriegel, Jörg Sander, & Xiaowei Xu. (1996). A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise. Knowledge Discovery and Data Mining. 226–231. 810 indexed citations breakdown →
19.
Ester, Martin, Hans‐Peter Kriegel, Jörg Sander, & Xiaowei Xu. (1996). A density-based algorithm for discovering clusters in large spatial Databases with Noise. Knowledge Discovery and Data Mining. 226–231. 12527 indexed citations breakdown →
20.
Sander, Jörg. (1970). [Induction of malignant tumors in rats by oral administration of N,N'-dimethylurea and nitrite].. PubMed. 20(3). 418–9. 12 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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