Stream Mining

Stream Mining is a computational paradigm that involves the continuous extraction of meaningful patterns and knowledge from high-velocity, time-varying data streams, distinguished by its requirement for real-time processing, limited memory usage, and single-pass analysis of transient data elements.

1.6K

Publications

133.9K

Citations

3.3K

Authors

1.1K

Institutions

Publications per year

2017–2026

507

Authors

3.3K

  1. PS
    Philip S. Yu

    University of Illinois Chicago, United States

    36 publications · 4.9K citations

    31

    h-index

  2. JG
    João Gama

    Universidade do Porto, Portugal

    42 publications · 4.3K citations

    27

    h-index

  3. AB
    Albert Bifet

    University of Waikato, New Zealand

    44 publications · 6.6K citations

    27

    h-index

  4. MM
    Mohamed Medhat Gaber

    Birmingham City University, United Kingdom

    27 publications · 1.9K citations

    20

    h-index

  5. XW
    Xindong Wu

    Hefei University of Technology, China

    24 publications · 1K citations

    19

    h-index

1–5 of 3.3K

Venues

  1. Information Sciences

    61 publications · 3.4K citations

    32

    h-index

  2. Expert Systems with Applications

    57 publications · 2.8K citations

    30

    h-index

  3. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 36 publications · 3.6K citations

    29

    h-index

  4. Knowledge and Information Systems

    32 publications · 2.2K citations

    24

    h-index

  5. Data Mining and Knowledge Discovery

    25 publications · 2.6K citations

    20

    h-index

1–5