Top-k Frequent Pattern Mining

Top-K Frequent Pattern Mining is a frequent pattern mining approach that identifies the k most frequent itemsets exceeding all others in support count, without requiring a user-specified minimum support threshold.

105

Publications

6.3K

Citations

274

Authors

146

Institutions

Publications per year

2017–2026

39

Variants

Top-k Pattern Mining

Authors

274

  1. PF
    Philippe Fournier‐Viger

    Harbin Institute of Technology, China

    7 publications · 305 citations

    7

    h-index

  2. ZD
    Zhi‐Hong Deng

    Peking University, China

    5 publications · 142 citations

    5

    h-index

  3. JH
    Jiawei Han

    University of Illinois Urbana-Champaign, United States

    5 publications · 1.9K citations

    5

    h-index

  4. TL
    Tuong Le

    Ton Duc Thang University, Vietnam

    5 publications · 168 citations

    5

    h-index

  5. XY
    Xifeng Yan

    University of California, Santa Barbara, United States

    5 publications · 1.8K citations

    5

    h-index

1–5 of 274

Venues

  1. Expert Systems with Applications

    11 publications · 322 citations

    11

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  2. Applied Intelligence

    9 publications · 283 citations

    8

    h-index

  3. Information Sciences

    6 publications · 298 citations

    6

    h-index

  4. Knowledge and Information Systems

    6 publications · 270 citations

    6

    h-index

  5. Data Mining and Knowledge Discovery

    5 publications · 1.6K citations

    5

    h-index

1–5