Rule Interestingness Evaluation

Rule Interestingness Evaluation is a formal metric used in pattern evaluation to quantify the degree of unexpectedness, usefulness, or relevance of an association rule based on statistical, cognitive, or application-specific criteria that distinguish it from trivial or redundant rules.

134

Publications

6.9K

Citations

313

Authors

171

Institutions

Publications per year

2017–2026

10

Authors

313

  1. FG
    Fabrice Guillet

    Laboratoire d'informatique de Nantes Atlantique, France

    5 publications · 186 citations

    5

    h-index

  2. HB
    H. Briand

    Rutherford Appleton Laboratory, United Kingdom

    5 publications · 186 citations

    5

    h-index

  3. BH
    B. H. Shekar

    Mangalore University, India

    4 publications · 81 citations

    4

    h-index

  4. RN
    Rajesh Natarajan

    Indian Institute of Management Bangalore, India

    4 publications · 81 citations

    4

    h-index

  5. SL
    Stéphane Lallich

    Université Claude Bernard Lyon 1, France

    4 publications · 242 citations

    4

    h-index

1–5 of 313

Venues

  1. Information Sciences

    4 publications · 190 citations

    4

    h-index

  2. Knowledge-Based Systems

    3 publications · 343 citations

    3

    h-index

  3. Expert Systems with Applications

    2 publications · 72 citations

    2

    h-index

  4. Applied Soft Computing

    2 publications · 147 citations

    2

    h-index

  5. Kent Academic Repository (University of Kent)

    Repository · 2 publications · 35 citations

    2

    h-index

1–5