Incremental Frequent Pattern Mining

Incremental Frequent Pattern Mining is a computational method in the field of data mining that dynamically updates the set of frequent patterns in response to changes in the transaction database, thereby avoiding the need to recompute patterns from scratch.

205

Publications

14.2K

Citations

462

Authors

201

Institutions

Publications per year

2017–2026

51

Authors

462

  1. UY
    Unil Yun

    Sejong University, South Korea

    17 publications · 767 citations

    16

    h-index

  2. TH
    Tzung‐Pei Hong

    National University of Kaohsiung, Taiwan

    19 publications · 1K citations

    14

    h-index

  3. JC
    Jerry Chun‐Wei Lin

    Western Norway University of Applied Sciences, Norway

    17 publications · 771 citations

    14

    h-index

  4. SL
    Suh-Yin Lee

    National Yang Ming Chiao Tung University, Taiwan

    9 publications · 301 citations

    9

    h-index

  5. MC
    Ming-Syan Chen⋆

    National Taiwan University, Taiwan

    7 publications · 495 citations

    7

    h-index

1–5 of 462

Venues

  1. Expert Systems with Applications

    13 publications · 787 citations

    13

    h-index

  2. IEEE Access

    8 publications · 205 citations

    8

    h-index

  3. Applied Intelligence

    8 publications · 292 citations

    8

    h-index

  4. Knowledge-Based Systems

    8 publications · 255 citations

    7

    h-index

  5. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 6 publications · 942 citations

    6

    h-index

1–5