Timm Jansen
Impact in
- Signal Processing top 10%
- Time Series Analysis and Forecasting
- Data Management and Algorithms
- Artificial Intelligence top 5%
- Data Stream Mining Techniques
- Anomaly Detection Techniques and Applications
- Advanced Clustering Algorithms Research
- Machine Learning and Data Classification
Papers in
-
- Data Stream Mining Techniques 4
- Advanced Clustering Algorithms Research 3
- Anomaly Detection Techniques and Applications 2
- Machine Learning and Data Classification 1
-
- Data Mining Algorithms and Applications 3
- Co-authors
- Thomas Seidl (6 shared papers)Bernhard Pfahringer (4 shared papers)Geoffrey Holmes (4 shared papers)Albert Bifet (4 shared papers)Hardy Kremer (4 shared papers)Philipp Kranen (3 shared papers)Emmanuel Müller (2 shared papers)Ira Assent (2 shared papers)
- Journals
- RWTH Publications (RWTH Aachen) (5 papers)VBN Forskningsportal (Aalborg Universitet) (1 paper)
- Partner nations
- GermanyNew ZealandDenmark
In The Last Decade
Timm Jansen
6 papers receiving 225 citations
Peers
Comparison fields: 5 of 39
- Signal Processing 86
- Artificial Intelligence 212
- Computer Networks and Communications 40
- Information Systems 38
- Computer Vision and Pattern Recognition 32
Countries citing papers authored by Timm Jansen
This map shows the geographic impact of Timm Jansen'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 Timm Jansen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Timm Jansen more than expected).
Fields of papers citing papers by Timm Jansen
This network shows the impact of papers produced by Timm Jansen. 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 Timm Jansen. The network helps show where Timm Jansen may publish in the future.
Co-authors
The 10 scholars most cited alongside Timm Jansen, 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 | MOA: Massive Online Analysis, a framework for stream classification and clustering. | 2010 | 135 |
| 2 | 2011 | 56 | |
| 3 | 2008 | 29 | |
| 4 | 2010 | 14 | |
| 5 | A framework for evaluation and exploration of clustering algorithms in subspaces of high dimensional databases | 2011 | 5 |
| 6 | Benchmarking Stream Clustering Algorithms within the MOA Framework | 2010 | 2 |
About Timm Jansen
Timm Jansen is a scholar working on Artificial Intelligence, Information Systems, Signal Processing, Computer Vision and Pattern Recognition and Infectious Diseases, having authored 6 papers that have together received 241 indexed citations. Recurring topics across this work include Data Stream Mining Techniques (4 papers), Advanced Clustering Algorithms Research (3 papers), Data Mining Algorithms and Applications (3 papers), Anomaly Detection Techniques and Applications (2 papers), Data Management and Algorithms (1 paper), Data Visualization and Analytics (1 paper), Time Series Analysis and Forecasting (1 paper) and Machine Learning and Data Classification (1 paper). The work is most often cited by research in Signal Processing (86 citations), Artificial Intelligence (212 citations), Computer Networks and Communications (40 citations), Information Systems (38 citations) and Computer Vision and Pattern Recognition (32 citations). Timm Jansen has collaborated with scholars based in Germany, New Zealand and Denmark. Frequent co-authors include Thomas Seidl, Bernhard Pfahringer, Geoffrey Holmes, Albert Bifet, Hardy Kremer, Philipp Kranen, Emmanuel Müller, Ira Assent, Ralph Krieger and Stephan Günnemann. Their work appears in journals such as RWTH Publications (RWTH Aachen) and VBN Forskningsportal (Aalborg Universitet).
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.