Data Stream Mining

Data Stream Mining is a computational method that systematically extracts, analyzes, and interprets continuous, real-time flows of user-generated data from social media platforms to identify patterns, behaviors, and trends as they emerge, distinguishing it from batch-oriented data analysis techniques by its emphasis on temporal immediacy and sequential processing.

2.1K

Publications

184K

Citations

5K

Authors

1.5K

Institutions

Publications per year

2017–2026

676

Authors

5K

  1. PS
    Philip S. Yu

    University of Illinois Chicago, United States

    37 publications · 10.1K citations

    32

    h-index

  2. AB
    Albert Bifet

    University of Waikato, New Zealand

    34 publications · 6.3K citations

    25

    h-index

  3. MM
    Mohamed Medhat Gaber

    Birmingham City University, United Kingdom

    28 publications · 2K citations

    21

    h-index

  4. JG
    João Gama

    Universidade do Porto, Portugal

    24 publications · 3.1K citations

    20

    h-index

  5. XW
    Xindong Wu

    Hefei University of Technology, China

    24 publications · 6.8K citations

    20

    h-index

1–5 of 5K

Venues

  1. Expert Systems with Applications

    41 publications · 2.1K citations

    27

    h-index

  2. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 32 publications · 3.6K citations

    27

    h-index

  3. Information Sciences

    36 publications · 2.2K citations

    26

    h-index

  4. Knowledge and Information Systems

    24 publications · 7.2K citations

    20

    h-index

  5. arXiv (Cornell University)

    Repository · 34 publications · 2K citations

    19

    h-index

1–5