Jilles Vreeken
- Artificial Intelligence top 1%
- Information Systems top 1%
- Statistical and Nonlinear Physics top 2%
- Computational Theory and Mathematics top 1%
- Signal Processing top 2%
- Co-authors
- Christos FaloutsosArno SiebesMatthijs van LeeuwenB. Aditya PrakashPauli MiettinenKoen SmetsNikolaj TattiMario Boley
- Topics
- Data Mining Algorithms and Applications (41 papers)Bayesian Modeling and Causal Inference (25 papers)Rough Sets and Fuzzy Logic (22 papers)
- Partner nations
- GermanyBelgiumUnited States
In The Last Decade
Jilles Vreeken
101 papers receiving 2.2k citations
Peers
Comparison fields: 5 of 141
- Artificial Intelligence 1.1k
- Information Systems 675
- Statistical and Nonlinear Physics 386
- Computational Theory and Mathematics 376
- Signal Processing 349
Countries citing papers authored by Jilles Vreeken
This map shows the geographic impact of Jilles Vreeken'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 Jilles Vreeken with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jilles Vreeken more than expected).
Fields of papers citing papers by Jilles Vreeken
This network shows the impact of papers produced by Jilles Vreeken. 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 Jilles Vreeken. The network helps show where Jilles Vreeken may publish in the future.
Co-authorship network of co-authors of Jilles Vreeken
This figure shows the co-authorship network connecting the top 25 collaborators of Jilles Vreeken. A scholar is included among the top collaborators of Jilles Vreeken 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 Jilles Vreeken. Jilles Vreeken is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 1 | |
| 3 | 1 | |
| 4 | 11 | |
| 5 | 57 | |
| 6 | 3 | |
| 7 | 11 | |
| 8 | Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2016) | 15 |
| 9 | 18 | |
| 10 | Supporting exploratory search through user modeling | 1 |
| 11 | 40 | |
| 12 | Proceedings of the ACM SIGKDD Workshop on Outlier Detection and Description | 17 |
| 13 | 38 | |
| 14 | 147 | |
| 15 | When Pattern Met Subspace Cluster. | 1 |
| 16 | 14 | |
| 17 | 17 | |
| 18 | Intelligent Traffic Light Control | 118 |
| 19 | Spiking neural networks, an introduction | 86 |
| 20 | Dynamic neural networks, comparing spiking circuits and LSTM | 3 |
About Jilles Vreeken
Jilles Vreeken is a scholar working on Artificial Intelligence, Information Systems and Computational Theory and Mathematics, having authored 107 papers that have together received 2.2k indexed citations. Recurring topics across this work include Data Mining Algorithms and Applications (41 papers), Bayesian Modeling and Causal Inference (25 papers) and Rough Sets and Fuzzy Logic (22 papers). The work is most often cited by research in Computational Mathematics (24 citations), Artificial Intelligence (1.1k citations) and Signal Processing (349 citations). Jilles Vreeken has collaborated with scholars based in Germany, Belgium and United States. Frequent co-authors include Christos Faloutsos, Arno Siebes, Matthijs van Leeuwen, B. Aditya Prakash, Pauli Miettinen, Koen Smets, Nikolaj Tatti, Mario Boley, Luca M. Ghiringhelli and U Kang. Their work appears in journals such as Nucleic Acids Research, Nature Communications and Bioinformatics.
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.