Ines Färber
Impact in
- Signal Processing top 5%
- Data Management and Algorithms
-
- Complex Network Analysis Techniques
Papers in
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- Advanced Clustering Algorithms Research 14
- Bayesian Methods and Mixture Models 3
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- Face and Expression Recognition 4
- Data Visualization and Analytics 4
- Co-authors
- Thomas Seidl (18 shared papers)Stephan Günnemann (13 shared papers)Emmanuel Müller (5 shared papers)Brigitte Boden (2 shared papers)Andrada Tatu (2 shared papers)Enrico Bertini (1 shared paper)Daniel A. Keim (2 shared papers)Tobias Schreck (2 shared papers)
- Journals
- Knowledge and Information Systems (1 paper)Proceedings of the VLDB Endowment (1 paper)RWTH Publications (RWTH Aachen) (3 papers)Datenschutz und Datensicherheit - DuD (1 paper)KOPS (University of Konstanz) (1 paper)
- Partner nations
- GermanyUnited StatesIndia
In The Last Decade
Ines Färber
18 papers receiving 443 citations
Peers
Comparison fields: 5 of 65
- Signal Processing 151
- Statistical and Nonlinear Physics 143
- Computational Mathematics 6
- Artificial Intelligence 323
- Computer Vision and Pattern Recognition 164
Countries citing papers authored by Ines Färber
This map shows the geographic impact of Ines Färber'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 Ines Färber with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ines Färber more than expected).
Fields of papers citing papers by Ines Färber
This network shows the impact of papers produced by Ines Färber. 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 Ines Färber. The network helps show where Ines Färber may publish in the future.
Co-authors
The 24 scholars most cited alongside Ines Färber, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2010 | 73 | |
| 2 | On Using Class-Labels in Evaluation of Clusterings | 2010 | 69 |
| 3 | 2012 | 68 | |
| 4 | 2012 | 43 | |
| 5 | 2013 | 37 | |
| 6 | 2011 | 33 | |
| 7 | 2009 | 29 | |
| 8 | 2012 | 23 | |
| 9 | 2010 | 22 | |
| 10 | 2013 | 21 | |
| 11 | 2014 | 17 | |
| 12 | 2012 | 10 | |
| 13 | 2011 | 4 | |
| 14 | 2010 | 4 | |
| 15 | 2010 | 4 | |
| 16 | Visual Quality Assessment of Subspace Clusterings | 2016 | 3 |
| 17 | Efficient database techniques for identification with fuzzy vault templates | 2011 | 1 |
| 18 | Filtertechniken für geschützte biometrische Datenbanken. | 2011 | 1 |
About Ines Färber
Ines Färber is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing, Information Systems and Statistical and Nonlinear Physics, having authored 18 papers that have together received 462 indexed citations. Recurring topics across this work include Advanced Clustering Algorithms Research (14 papers), Data Management and Algorithms (6 papers), Complex Network Analysis Techniques (5 papers), Face and Expression Recognition (4 papers), Data Mining Algorithms and Applications (4 papers), Data Visualization and Analytics (4 papers), Bayesian Methods and Mixture Models (3 papers) and Biometric Identification and Security (2 papers). The work is most often cited by research in Signal Processing (151 citations), Statistical and Nonlinear Physics (143 citations), Computational Mathematics (6 citations), Artificial Intelligence (323 citations) and Computer Vision and Pattern Recognition (164 citations). Ines Färber has collaborated with scholars based in Germany, United States and India. Frequent co-authors include Thomas Seidl, Stephan Günnemann, Emmanuel Müller, Brigitte Boden, Andrada Tatu, Enrico Bertini, Daniel A. Keim, Tobias Schreck, Sebastian Raubach and Erich Schubert. Their work appears in journals such as Knowledge and Information Systems, Proceedings of the VLDB Endowment, RWTH Publications (RWTH Aachen), Datenschutz und Datensicherheit - DuD and KOPS (University of Konstanz).
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.