Ines Färber
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 5%
- Signal Processing top 5%
- Statistical and Nonlinear Physics top 5%
- Information Systems top 10%
- Co-authors
- Thomas SeidlStephan GünnemannEmmanuel MüllerBrigitte BodenEnrico BertiniTobias SchreckAndrada TatuDaniel A. Keim
- Topics
- Advanced Clustering Algorithms Research (14 papers)Data Management and Algorithms (6 papers)Complex Network Analysis Techniques (5 papers)
- Journals
- Proceedings of the VLDB EndowmentKnowledge and Information SystemsKOPS (University of Konstanz)
- Partner nations
- GermanyUnited StatesIndia
In The Last Decade
Ines Färber
18 papers receiving 443 citations
Peers
Comparison fields: 5 of 65
- Artificial Intelligence 323
- Computer Vision and Pattern Recognition 164
- Signal Processing 151
- Statistical and Nonlinear Physics 143
- Information Systems 98
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-authorship network of co-authors of Ines Färber
This figure shows the co-authorship network connecting the top 25 collaborators of Ines Färber. A scholar is included among the top collaborators of Ines Färber 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 Ines Färber. Ines Färber is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Visual Quality Assessment of Subspace Clusterings | 3 |
| 2 | 17 | |
| 3 | 21 | |
| 4 | 37 | |
| 5 | 10 | |
| 6 | 23 | |
| 7 | 68 | |
| 8 | 43 | |
| 9 | Efficient database techniques for identification with fuzzy vault templates | 1 |
| 10 | Filtertechniken für geschützte biometrische Datenbanken. | 1 |
| 11 | 4 | |
| 12 | 33 | |
| 13 | On Using Class-Labels in Evaluation of Clusterings | 69 |
| 14 | 22 | |
| 15 | 73 | |
| 16 | 4 | |
| 17 | 4 | |
| 18 | 29 |
About Ines Färber
Ines Färber is a scholar working on Signal Processing, Artificial Intelligence 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) and Complex Network Analysis Techniques (5 papers). The work is most often cited by research in Signal Processing (151 citations), Statistical and Nonlinear Physics (143 citations) and Computational Mathematics (6 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, Enrico Bertini, Tobias Schreck, Andrada Tatu, Daniel A. Keim, Sebastian Raubach and Hans‐Peter Kriegel. Their work appears in journals such as Proceedings of the VLDB Endowment, Knowledge and Information Systems 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.