Maximal Frequent Itemset Mining

Maximal Frequent Itemset Mining is a method in frequent pattern mining that identifies the smallest complete set of frequent itemsets by retaining only those itemsets for which no proper superset is frequent, thereby eliminating redundancy while preserving the support information of all frequent itemsets.

186

Publications

13.4K

Citations

462

Authors

207

Institutions

Publications per year

2017–2026

35

Authors

462

  1. UY
    Unil Yun

    Sejong University, South Korea

    9 publications · 513 citations

    9

    h-index

  2. MJ
    Mohammed J. Zaki

    Rensselaer Polytechnic Institute, United States

    7 publications · 2.1K citations

    7

    h-index

  3. GL
    Gangin Lee

    Sejong University, South Korea

    6 publications · 289 citations

    6

    h-index

  4. KH
    Keun Ho Ryu

    Chungbuk National University, South Korea

    5 publications · 387 citations

    5

    h-index

  5. SM
    Soon M. Chung

    Wright State University, United States

    5 publications · 138 citations

    5

    h-index

1–5 of 462

Venues

  1. Expert Systems with Applications

    11 publications · 552 citations

    11

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  2. Knowledge and Information Systems

    11 publications · 396 citations

    11

    h-index

  3. Knowledge-Based Systems

    8 publications · 350 citations

    8

    h-index

  4. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 7 publications · 1.1K citations

    7

    h-index

  5. Information Sciences

    4 publications · 281 citations

    4

    h-index

1–5