Streaming Data Mining

Streaming Data Mining is a methodological framework within scalable and temporal data mining that enables the continuous, real-time analysis of high-volume, time-ordered data sequences by processing each element exactly once or within strict latency constraints, thereby distinguishing it from batch or static data mining approaches.

948

Publications

99K

Citations

2K

Authors

743

Institutions

Publications per year

2017–2026

258

Authors

2K

  1. PS
    Philip S. Yu

    University of Illinois Chicago, United States

    26 publications · 9.6K citations

    24

    h-index

  2. AB
    Albert Bifet

    University of Waikato, New Zealand

    28 publications · 5.4K citations

    22

    h-index

  3. XW
    Xindong Wu

    Hefei University of Technology, China

    22 publications · 6.7K citations

    19

    h-index

  4. JG
    João Gama

    Universidade do Porto, Portugal

    24 publications · 1.8K citations

    19

    h-index

  5. MM
    Mohamed Medhat Gaber

    Birmingham City University, United Kingdom

    20 publications · 1.6K citations

    18

    h-index

1–5 of 2K

Venues

  1. Information Sciences

    34 publications · 2.1K citations

    26

    h-index

  2. Expert Systems with Applications

    42 publications · 2.2K citations

    26

    h-index

  3. Knowledge and Information Systems

    28 publications · 7.3K citations

    21

    h-index

  4. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 17 publications · 1.6K citations

    17

    h-index

  5. Intelligent Data Analysis

    IDA · 17 publications · 646 citations

    15

    h-index

1–5