High-utility Sequential Pattern Mining

High-Utility Sequential Pattern Mining is a data mining methodology that identifies sequences of events or items with significantly high utility values, where utility reflects user-defined measures such as profit, importance, or weight, thereby extending traditional sequential pattern mining by incorporating quantitative utility information to discover more meaningful and valuable patterns.

81

Publications

4.2K

Citations

178

Authors

105

Institutions

Publications per year

2017–2026

55

Authors

178

  1. JC
    Jerry Chun‐Wei Lin

    Western Norway University of Applied Sciences, Norway

    17 publications · 1.1K citations

    16

    h-index

  2. PF
    Philippe Fournier‐Viger

    Harbin Institute of Technology, China

    15 publications · 925 citations

    15

    h-index

  3. WG
    Wensheng Gan

    Jinan University, China

    9 publications · 349 citations

    8

    h-index

  4. BL
    Bac Le

    Ho Chi Minh City University of Science, Vietnam

    8 publications · 198 citations

    8

    h-index

  5. AA
    Aijun An

    York University, Canada

    6 publications · 172 citations

    6

    h-index

1–5 of 178

Venues

  1. Expert Systems with Applications

    13 publications · 950 citations

    12

    h-index

  2. Information Sciences

    9 publications · 359 citations

    9

    h-index

  3. Applied Intelligence

    9 publications · 392 citations

    9

    h-index

  4. Knowledge-Based Systems

    7 publications · 294 citations

    7

    h-index

  5. ACM Transactions on Knowledge Discovery from Data

    TKDD · 3 publications · 68 citations

    3

    h-index

1–5