Approximate Frequent Pattern Mining

Approximate Frequent Pattern Mining is a method in data mining that identifies frequent patterns under relaxed support constraints, allowing for the discovery of itemsets that approximately satisfy minimum frequency thresholds amidst noise or data incompleteness.

67

Publications

3.5K

Citations

163

Authors

77

Institutions

Publications per year

2017–2026

11

Authors

163

  1. UY
    Unil Yun

    Sejong University, South Korea

    4 publications · 141 citations

    4

    h-index

  2. WW
    Wei Wang

    University of California, Los Angeles, United States

    3 publications · 109 citations

    3

    h-index

  3. SO
    Salvatore Orlando

    Ca' Foscari University of Venice, Italy

    3 publications · 96 citations

    3

    h-index

  4. PS
    Philip S. Yu

    University of Illinois Chicago, United States

    2 publications · 71 citations

    2

    h-index

  5. AF
    Avrilia Floratou

    University of Wisconsin–Madison, United States

    2 publications · 69 citations

    2

    h-index

1–5 of 163

Venues

  1. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 6 publications · 378 citations

    6

    h-index

  2. Expert Systems with Applications

    3 publications · 52 citations

    3

    h-index

  3. Knowledge-Based Systems

    2 publications · 94 citations

    2

    h-index

  4. Fundamenta Informaticae

    2 publications · 29 citations

    2

    h-index

  5. 2

    h-index

1–5