Incremental Data Mining

Incremental Data Mining is a data mining paradigm that belongs to the class of scalable data mining techniques, distinguished by its ability to update models or mine patterns efficiently from newly arriving data without reprocessing the entire dataset from scratch.

282

Publications

16.5K

Citations

694

Authors

302

Institutions

Publications per year

2017–2026

61

Authors

694

  1. TH
    Tzung‐Pei Hong

    National University of Kaohsiung, Taiwan

    14 publications · 631 citations

    13

    h-index

  2. JC
    Jerry Chun‐Wei Lin

    Western Norway University of Applied Sciences, Norway

    13 publications · 539 citations

    13

    h-index

  3. UY
    Unil Yun

    Sejong University, South Korea

    12 publications · 438 citations

    11

    h-index

  4. BV
    Bay Vo

    Ho Chi Minh City University of Technology, Vietnam

    6 publications · 127 citations

    6

    h-index

  5. MC
    Ming-Syan Chen⋆

    National Taiwan University, Taiwan

    5 publications · 373 citations

    5

    h-index

1–5 of 694

Venues

  1. Expert Systems with Applications

    9 publications · 460 citations

    9

    h-index

  2. Knowledge-Based Systems

    9 publications · 274 citations

    8

    h-index

  3. arXiv (Cornell University)

    Repository · 7 publications · 181 citations

    7

    h-index

  4. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 7 publications · 1K citations

    7

    h-index

  5. Applied Intelligence

    7 publications · 233 citations

    7

    h-index

1–5