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Topic · Computational Theory and Mathematics

Rough Sets and Fuzzy Logic

Rough Sets and Fuzzy Logic is a research topic within Computational Theory and Mathematics. Science Explorer counts 36k research works in it since 1950. 10.0% of them reached the world's top 10% most cited for their field and year.

This cluster of papers explores the theory and applications of rough sets, granular computing, feature selection, decision analysis, information granulation, fuzzy rough sets, concept lattices, knowledge reduction, three-way decisions, and probabilistic rough sets in various domains.

  • Rough Sets
  • Granular Computing
  • Feature Selection
  • Decision Analysis
  • Information Granulation
  • Fuzzy Rough Sets
  • Concept Lattices
  • Knowledge Reduction
  • Three-Way Decisions
  • Probabilistic Rough Sets
Research works
36k
fractional, since 1950
In the world top 10%
3.6k
per year above
Top-10% rate
10.0%
share of its works in the world top 10%
Growth, 2018-21 → 2022-25
-9%
the tick is no change
Largest producer
China
59% of the 10 largest, 2022-now
Rough Sets and Fuzzy Logic

Which countries lead Rough Sets and Fuzzy Logic research?

By volume, China and India publish the most (1.7k and 356 works in 2022-now).

By volume, 2022-now

  1. 1 China 1.7k works
  2. 2 India 356 works
  3. 3 United States 173 works
  4. 4 Türkiye 123 works
  5. 5 Pakistan 101 works
  6. 6 Spain 98 works
  7. 7 France 97 works
  8. 8 Poland 94 works
  9. 9 Saudi Arabia 84 works
  10. 10 Japan 74 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: 59.1%India: 12.1%United States: 5.9%Türkiye: 4.2%6 others listed: 18.7%59%largest
China1,733 · 59.1%India356 · 12.1%United States173 · 5.9%Türkiye123 · 4.2%6 others listed548 · 18.7%

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

Which institutions lead Rough Sets and Fuzzy Logic research?

By volume in 2022-now, Sichuan University publishes the most Rough Sets and Fuzzy Logic research, followed by Centre National de la Recherche Scientifique and Chongqing University of Posts and Telecommunications.

By volume, 2022-now

  1. 1 Sichuan UniversityChina 37 works
  2. 2 Centre National de la Recherche ScientifiqueFrance 33 works
  3. 3 Chongqing University of Posts and TelecommunicationsChina 30 works
  4. 4 Southwest UniversityChina 30 works
  5. 5 Southwest Jiaotong UniversityChina 29 works
  6. 6 Shanxi UniversityChina 23 works
  7. 7 Xidian UniversityChina 23 works
  8. 8 Anhui UniversityChina 23 works
  9. 9 Sichuan Normal UniversityChina 22 works
  10. 10 Minnan Normal UniversityChina 22 works

Who are the leading researchers in Rough Sets and Fuzzy Logic?

The most-cited researchers publishing on Rough Sets and Fuzzy Logic include Steven L. Salzberg, Amos Tversky and Xiaogang Wang.

  1. 1 Steven L. Salzberg United States 372k citations
  2. 2 Amos Tversky United States 327k citations
  3. 3 Xiaogang Wang China 260k citations
  4. 4 Leo Breiman United States 243k citations
  5. 5 Kai You Wang China 224k citations
  6. 6 David H. Haussler United States 209k 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 Rough Sets and Fuzzy Logic?

The most cited paper in Rough Sets and Fuzzy Logic is "Nearest neighbor pattern classification" (Thomas M. Cover, Peter E. Hart, 1967), with 16,834 citations. Next come "Bagging predictors" (16,492) and "Mining association rules between sets of items in large databases" (14,855).

Most cited papers
#PaperReleasedCitations
1 Nearest neighbor pattern classification Thomas M. Cover, Peter E. Hart · IEEE Transactions on Information Theory 1967 16,834
2 Bagging predictors Leo Breiman · Machine Learning Open access Aug 1996 16,492
3 Mining association rules between sets of items in large databases Rakesh Agrawal, Tomasz Imieliński, Arun N. Swami Conference paperOpen access Jun 1993 14,855
4 Rough sets Zdzis aw Pawlak · International Journal of Parallel Programming Oct 1982 12,304
5 Fuzzy sets as a basis for a theory of possibility Lotfi A. Zadeh · Fuzzy Sets and Systems 1978 8,108
All 96 most cited papers, with filters and sorting

Where is Rough Sets and Fuzzy Logic research done?

The largest centres of Rough Sets and Fuzzy Logic research in 2022-now are Beijing (China), Chengdu (China), Xi'an (China) and Chongqing (China). Among places with at least 20 works in it, it is an unusually large share of all research in Zhangzhou.

Largest cities, 2022-now

  1. 1 BeijingChina 215 works
  2. 2 ChengduChina 134 works
  3. 3 Xi'anChina 130 works
  4. 4 ChongqingChina 89 works
  5. 5 NanjingChina 68 works
  6. 6 ShanghaiChina 67 works
  7. 7 ChangshaChina 64 works
  8. 8 GuangzhouChina 50 works
  9. 9 HefeiChina 47 works
  10. 10 JinanChina 42 works

Where it is the local speciality

  1. ZhangzhouCN · 22.7 works81×
← less than its size predicts1×more →

Location quotient: how much more of its research is in Rough Sets and Fuzzy Logic than the world average.

See Rough Sets and Fuzzy Logic on the map

Where is the best place to study Rough Sets and Fuzzy Logic?

Among universities, judged by research, Southwest University, Minzu University of China and Nantong 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 31.45%fractional works in this field (log) →share of work in the world top 10% →Nantong UniversitySouthwest UniversityMinzu University of …Shanxi 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
1Southwest UniversityChina 85.646.6%15.6×30 +54.0%
2Minzu University of ChinaChina 74.742.9%17.3×18 +37.4%
3Nantong UniversityChina 71.954.4%8.9×14 +77.5%
4Shanxi UniversityChina 71.027.2%24.4×23 +44.0%
5Hunan Normal UniversityChina 67.432.8%20.8×17 +113.4%
6Sichuan UniversityChina 61.021.1%4.6×37 -21.9%
7Chongqing University of Posts and TelecommunicationsChina 59.816.1%30.1×30 +11.7%
8Xidian UniversityChina 59.020.4%8.9×23 +42.5%
9Southwest Jiaotong UniversityChina 56.325.9%10.7×29 -16.8%
10Shaanxi University of Science and TechnologyChina 56.027.1%17.7×15 +52.3%

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 Rough Sets and Fuzzy Logic, country by country

Is Rough Sets and Fuzzy Logic research growing?

Output in 2022-2025 was 9% lower than in 2018-2021, peaking in 2009.

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 Rough Sets and Fuzzy Logic as a percentage of every work published that year, 0.019% in 2024.

still counting0.025%0.050%0.075%Rough Sets and Fuzzy Logic0.014% in 202519801990200020102020

Fractional works of Rough Sets and Fuzzy Logic 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%Rough Sets and Fuzzy Logic10.2% 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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