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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. 10.0% 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%
1.4k
per year above
Top-10% rate
10.0%
share of its works in the world top 10%
Growth, 2018-21 → 2022-25
+16%
the tick is no change
Largest producer
China
35% of the 10 largest, 2022-now
Data Stream Mining Techniques

Which countries lead Data Stream Mining Techniques research?

By volume, China and India publish the most (1.2k and 729 works in 2022-now).

By volume, 2022-now

  1. 1 China 1.2k works
  2. 2 India 729 works
  3. 3 United States 648 works
  4. 4 Germany 165 works
  5. 5 United Kingdom 144 works
  6. 6 Italy 118 works
  7. 7 Brazil 105 works
  8. 8 Canada 101 works
  9. 9 France 95 works
  10. 10 Australia 94 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: 34.9%India: 21.6%United States: 19.2%Germany: 4.9%6 others listed: 19.4%35%largest
China1,178 · 34.9%India729 · 21.6%United States648 · 19.2%Germany165 · 4.9%6 others listed656 · 19.4%

Shares of the rows listed above, not of the whole node.

Which institutions lead Data Stream Mining Techniques research?

By volume in 2022-now, Chinese Academy of Sciences publishes the most Data Stream Mining Techniques research, followed by Tsinghua University and SRM Institute of Science and Technology.

By volume, 2022-now

  1. 1 Chinese Academy of SciencesChina 40 works
  2. 2 Tsinghua UniversityChina 31 works
  3. 3 SRM Institute of Science and TechnologyIndia 26 works
  4. 4 Centre National de la Recherche ScientifiqueFrance 25 works
  5. 5 Peking UniversityChina 21 works
  6. 6 National University of Defense TechnologyChina 20 works
  7. 7 Shanghai Jiao Tong UniversityChina 19 works
  8. 8 University of Electronic Science and Technology of ChinaChina 17 works
  9. 9 Nanjing UniversityChina 17 works
  10. 10 University of Science and Technology of ChinaChina 17 works

Who are the leading researchers in Data Stream Mining Techniques?

The most-cited researchers publishing on Data Stream Mining Techniques include Yoshua Bengio, Geoffrey E. Hinton and Karl John Friston.

  1. 1 Yoshua Bengio Canada 476k citations
  2. 2 Geoffrey E. Hinton Canada 454k citations
  3. 3 Karl John Friston United Kingdom 310k citations
  4. 4 Li Fei-Fei United States 225k citations
  5. 5 Kai You Wang China 224k citations
  6. 6 Jürgen Schmidhuber Switzerland 188k citations
  7. 7 Michael I. Jordan United States 182k citations

Ranked by citations received across their whole record, among researchers with at least three works on this topic.

What are the most cited papers in Data Stream Mining Techniques?

The most cited paper in Data Stream Mining Techniques is "Anomaly detection" (Varun Chandola, Arindam Banerjee, Vipin Kumar, 2009), with 11,623 citations. Next come "Machine learning: Trends, perspectives, and prospects" (10,020) and "A survey of transfer learning" (6,327).

Most cited papers
#PaperReleasedCitations
1 Anomaly detection Varun Chandola, Arindam Banerjee, Vipin Kumar · ACM Computing Surveys Jul 2009 11,623
2 Machine learning: Trends, perspectives, and prospects Michael I. Jordan, Tom M. Mitchell · Science 16 Jul 2015 10,020
3 A survey of transfer learning Karl R. Weiss, Taghi M. Khoshgoftaar, Dingding Wang · Journal Of Big Data Open access 27 May 2016 6,327
4 Machine Learning: Algorithms, Real-World Applications and Research Directions Iqbal H. Sarker · SN Computer Science Open access 22 Mar 2021 5,413
5 Data mining and knowledge discovery: making sense out of data U.M. Feyyad · IEEE Expert Oct 1996 4,643
All 99 most cited papers, with filters and sorting

Where is Data Stream Mining Techniques research done?

The largest centres of Data Stream Mining Techniques research in 2022-now are Beijing (China), Shanghai (China), Chennai (India) and Nanjing (China).

Largest cities, 2022-now

  1. 1 BeijingChina 227 works
  2. 2 ShanghaiChina 80 works
  3. 3 ChennaiIndia 74 works
  4. 4 NanjingChina 63 works
  5. 5 GuangzhouChina 59 works
  6. 6 HangzhouChina 49 works
  7. 7 ShenzhenChina 47 works
  8. 8 BengaluruIndia 46 works
  9. 9 WuhanChina 44 works
  10. 10 ChengduChina 43 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 Zhejiang 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%mean 19.31%fractional works in this field (log) →share of work in the world top 10% →University of Techno…Zhejiang UniversitySouth China Universi…Tsinghua University
above the meannear itbelow it

One dot per university in the first ten of the table below, at the default weights. The upper left is the interesting corner: small places doing unusually strong work.

Excellenceshare of its work in the world top 10%, 2022-now and all time averaged
40%
Specialisationhow much of its work is in this field, counted up to 10× the world share
10%
Sizeits output in this field, on a log scale
30%
Growthoutput growth, counted from -20% to +20%
10%
Internationalshare of its work with a co-author abroad
10%
Each slider sets a weight; the share it gets of the score is beside it.
#UniversityScoreTop 10%SpecialisationWorksGrowth
1University of Technology SydneyAustralia 76.631.7%9.3×17 -8.6%
2Tsinghua UniversityChina 72.219.1%3.3×31 +50.7%
3Zhejiang UniversityChina 63.529.2%1.5×17 +142.5%
4South China University of TechnologyChina 56.226.5%2.5×11 +246.5%
5Peking UniversityChina 54.212.9%2.5×21 +88.2%
6University of Electronic Science and Technology of ChinaChina 51.212.0%3.8×18 +49.0%
7Central South UniversityChina 50.425.1%1.5×12 +110.1%
8University of Illinois Urbana-ChampaignUnited States 49.68.2%4.5×16 +64.2%
9University of Science and Technology of ChinaChina 49.211.7%3.5×17 +143.5%
10Chitkara UniversityIndia 47.216.7%6.8×11 +68.0%

Universities only. The score blends excellence (40%), size (30%), specialisation (10%), growth (10%), international reach (10%), 2022-now; growth compares 2018-21 with 2022-25 and counts up to +20%, since a jump from a small base is mostly noise.

Every institution in Data Stream Mining Techniques, country by country

Is Data Stream Mining Techniques research growing?

Output in 2022-2025 was 16% higher than in 2018-2021, peaking in 2023. The fastest-growing topics are Data Stream Mining Techniques.

still counting19801990200020102020
grewheldshrank

The same series as a ribbon: one cell per year, darker for more. The line above answers how much; this answers when.

Its share of all research

Growing output can still be a shrinking share: all of science grows. This is Data Stream Mining Techniques as a percentage of every work published that year, 0.019% in 2024.

still counting0.010%0.020%0.030%Data Stream Mining Techniques0.020% in 202519801990200020102020

Fractional works of Data Stream Mining Techniques over all fractional works that year. How the balance of science has shifted.

How much of it reaches the world top 10%

Year by year.

still counting5%10%15%Data Stream Mining Techniques10.0% in 2025200020102020

Of each year's works in it, the share that reached the world's 10% most cited for their field and year. A field is the world for its own topics, so there is no other line to hold it against.

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.

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