Data Stream Mining Techniques
Data Stream Mining Techniques is a research topic within Artificial Intelligence. Science Explorer counts 14k research works in it since 1958. 25.7% of them reached the world's top 10% most cited for their field and year.
This cluster of papers focuses on the adaptation to concept drift in data streams, particularly in the context of ensemble learning, adaptive algorithms, and online learning. It addresses challenges such as change detection, class imbalance, and incremental learning in streaming data environments.
- Concept Drift
- Data Streams
- Ensemble Learning
- Adaptive Algorithms
- Online Learning
- Change Detection
- Class Imbalance
- Streaming Data
- Incremental Learning
- Ensemble Classifiers
- Research works
- 14k fractional, since 1958
- In the world top 10%
- 3.6k per year above
- Top-10% rate
- 25.7% share of its works in the world top 10%
- Growth, 2013–17 → 2018–22
- +74% the tick is no change
Which countries lead Data Stream Mining Techniques research?
By volume, China and India publish the most (1k and 672 works in 2022–2025).
By volume, 2022–2025
- 1 China 1k works
- 2 India 672 works
- 3 United States 578 works
- 4 Germany 153 works
- 5 United Kingdom 132 works
- 6 Italy 109 works
- 7 Brazil 98 works
- 8 Canada 95 works
- 9 France 88 works
- 10 Australia 86 works
How concentrated that is
The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.
Shares of the rows listed above, not of the whole node.
Which institutions lead Data Stream Mining Techniques research?
By volume in 2022–2025, Tsinghua University publishes the most Data Stream Mining Techniques research, followed by SRM Institute of Science and Technology and National University of Defense Technology.
By volume, 2022–2025
- 1 Tsinghua University China 29 works
- 2 SRM Institute of Science and Technology India 20 works
- 3 National University of Defense Technology China 18 works
- 4 Peking University China 18 works
- 5 Shanghai Jiao Tong University China 17 works
- 6 University of Science and Technology of China China 16 works
- 7 University of Electronic Science and Technology of China China 16 works
- 8 Nanjing University China 16 works
- 9 University of Technology Sydney Australia 15 works
- 10 Politecnico di Milano Italy 15 works
Who are the leading researchers in Data Stream Mining Techniques?
The most-cited researchers publishing on Data Stream Mining Techniques include Wei Liu, Ion Stoica and Rajkumar Buyya.
- 1 Wei Liu 9.7k citations
- 2 Ion Stoica 9.6k citations
- 3 Rajkumar Buyya 7.8k citations
- 4 Philip S. Yu 6.6k citations
- 5 Francisco Herrera 6.5k citations
- 6 Dacheng Tao 6.1k citations
- 7 Witold Pedrycz 5.4k citations
- 8 Jiawei Han 5.2k citations
- 9 Bernhard Schölkopf 4.7k citations
Ranked by citations received across their whole record, among researchers with at least three works on this topic.
Where is Data Stream Mining Techniques research done?
The largest centres of Data Stream Mining Techniques research in 2022–2025 are Beijing (China), Shanghai (China), Chennai (India) and Nanjing (China).
Where is the best place to study Data Stream Mining Techniques?
Among universities, judged by research, University of Technology Sydney, Tsinghua University and Bielefeld University score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.
One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.
| # | University | Score | Top 10% | Specialisation | Works | Growth |
|---|---|---|---|---|---|---|
| 1 | University of Technology Sydney Australia | 76.2 | 50.7% | 7.9× | 15 | +110.5% |
| 2 | Tsinghua University China | 59.3 | 35.9% | 3.6× | 29 | +114.3% |
| 3 | Bielefeld University Germany | 54.4 | 21.9% | 9.6× | 9 | +289.8% |
| 4 | South China University of Technology China | 50.9 | 46.2% | 2.2× | 10 | +225.8% |
| 5 | Chandigarh University India | 46.0 | 28.0% | 6.3× | 14 | — |
| 6 | Nanjing University China | 45.3 | 20.0% | 4.9× | 16 | +163.8% |
| 7 | Shanghai Jiao Tong University China | 45.0 | 26.9% | 1.9× | 17 | +203.0% |
| 8 | University of Electronic Science and Technology of China China | 44.7 | 22.9% | 3.4× | 16 | +215.8% |
| 9 | University of Science and Technology of China China | 44.6 | 27.1% | 3.2× | 16 | +151.0% |
| 10 | Chitkara University India | 42.9 | 29.1% | 6.5× | 10 | — |
Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.
Is Data Stream Mining Techniques research growing?
Output in 2018–2022 was 74% higher than in 2013–2017, peaking in 2023. The fastest-growing topics are Data Stream Mining Techniques.
The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.
Which topics inside it are moving
Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.