Weighted Frequent Pattern Mining

Weighted Frequent Pattern Mining is a specialized method within frequent pattern mining that identifies frequently occurring itemsets in transactional databases by incorporating item-specific weights to reflect their relative importance, thereby prioritizing patterns based on both occurrence frequency and significance.

103

Publications

4.5K

Citations

222

Authors

131

Institutions

Publications per year

2017–2026

32

Variants

Weighted Frequent Itemset Mining

Authors

222

  1. UY
    Unil Yun

    Sejong University, South Korea

    20 publications · 932 citations

    20

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  2. BV
    Bay Vo

    Ho Chi Minh City University of Technology, Vietnam

    8 publications · 285 citations

    8

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  3. CF
    Chowdhury Farhan Ahmed

    University of Dhaka, Bangladesh

    7 publications · 317 citations

    7

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  4. JC
    Jerry Chun‐Wei Lin

    Western Norway University of Applied Sciences, Norway

    6 publications · 202 citations

    6

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  5. TH
    Tzung‐Pei Hong

    National University of Kaohsiung, Taiwan

    6 publications · 188 citations

    6

    h-index

1–5 of 222

Venues

  1. Expert Systems with Applications

    12 publications · 701 citations

    12

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  2. Applied Intelligence

    8 publications · 253 citations

    8

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  3. Knowledge-Based Systems

    6 publications · 243 citations

    6

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  4. Information Sciences

    5 publications · 255 citations

    5

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  5. IEEE Access

    4 publications · 68 citations

    4

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1–5