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Topic · Artificial Intelligence

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. 1 China 1k works
  2. 2 India 672 works
  3. 3 United States 578 works
  4. 4 Germany 153 works
  5. 5 United Kingdom 132 works
  6. 6 Italy 109 works
  7. 7 Brazil 98 works
  8. 8 Canada 95 works
  9. 9 France 88 works
  10. 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.

China: 33.3%India: 22.3%United States: 19.2%Germany: 5.1%6 others listed: 20.1%33%largest
China1,002 · 33.3%India672 · 22.3%United States578 · 19.2%Germany153 · 5.1%6 others listed607 · 20.1%

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. 1 Tsinghua UniversityChina 29 works
  2. 2 SRM Institute of Science and TechnologyIndia 20 works
  3. 3 National University of Defense TechnologyChina 18 works
  4. 4 Peking UniversityChina 18 works
  5. 5 Shanghai Jiao Tong UniversityChina 17 works
  6. 6 University of Science and Technology of ChinaChina 16 works
  7. 7 University of Electronic Science and Technology of ChinaChina 16 works
  8. 8 Nanjing UniversityChina 16 works
  9. 9 University of Technology SydneyAustralia 15 works
  10. 10 Politecnico di MilanoItaly 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. 1 Wei Liu China 9.7k citations
  2. 2 Ion Stoica United States 9.6k citations
  3. 3 Rajkumar Buyya Australia 7.8k citations
  4. 4 Philip S. Yu United States 6.6k citations
  5. 5 Francisco Herrera Spain 6.5k citations
  6. 6 Dacheng Tao Australia 6.1k citations
  7. 7 Witold Pedrycz Canada 5.4k citations
  8. 8 Jiawei Han United States 5.2k citations
  9. 9 Bernhard Schölkopf Germany 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).

Largest cities, 2022–2025

  1. 1 Beijing China 208 works
  2. 2 Shanghai China 70 works
  3. 3 Chennai India 64 works
  4. 4 Nanjing China 55 works
  5. 5 Guangzhou China 52 works
  6. 6 Hangzhou China 44 works
  7. 7 Shenzhen China 43 works
  8. 8 Bengaluru India 43 works
  9. 9 Wuhan China 41 works
  10. 10 Changsha China 39 works
See Data Stream Mining Techniques on the map

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.

0%20%40%60%mean 30.87%fractional works in this node (log) →share in the world top 10% →University of Technology Sydney: 15, 50.7%Tsinghua University: 29, 35.9%Bielefeld University: 9, 21.9%South China University of Technology: 10, 46.2%Chandigarh University: 14, 28.0%Nanjing University: 16, 20.0%Shanghai Jiao Tong University: 17, 26.9%University of Electronic Science and Technology of China: 16, 22.9%University of Science and Technology of China: 16, 27.1%Chitkara University: 10, 29.1%University of Techno…South China Universi…Tsinghua UniversityBielefeld University
above the meannear itbelow it

One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.

#UniversityScoreTop 10%SpecialisationWorksGrowth
1 University of Technology SydneyAustralia 76.250.7%7.9×15 +110.5%
2 Tsinghua UniversityChina 59.335.9%3.6×29 +114.3%
3 Bielefeld UniversityGermany 54.421.9%9.6×9 +289.8%
4 South China University of TechnologyChina 50.946.2%2.2×10 +225.8%
5 Chandigarh UniversityIndia 46.028.0%6.3×14 —
6 Nanjing UniversityChina 45.320.0%4.9×16 +163.8%
7 Shanghai Jiao Tong UniversityChina 45.026.9%1.9×17 +203.0%
8 University of Electronic Science and Technology of ChinaChina 44.722.9%3.4×16 +215.8%
9 University of Science and Technology of ChinaChina 44.627.1%3.2×16 +151.0%
10 Chitkara UniversityIndia 42.929.1%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.

19801990200020102020
grewheldshrank

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.