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
Parents
134
Finding interesting rules from large sets of discovered association rules
Mika Klemettinen, Heikki Mannila, P. Ronkainen et al. · 1994 · 719 citations
Mining the most interesting rules
Roberto J. Bayardo, Rakesh Agrawal · 1999 · 651 citations
On rule interestingness measures
Alex A. Freitas · Knowledge-Based Systems · 1999 · 236 citations
Analyzing the subjective interestingness of association rules
Bing Liu, Wynne Hsu, Shu Chen et al. · IEEE Intelligent Systems and their Applications · 2000 · 232 citations
1–5 of 134
313
Laboratoire d'informatique de Nantes Atlantique, France
5 publications · 186 citations
5
h-index
5
h-index
4
h-index
4
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4
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1–5 of 313
171

Centre National de la Recherche Scientifique
Paris, France
8 h-index
19 publications
1K citations

Universidad de Granada
Granada, Spain
5 h-index
17 publications
1K citations
National University of Singapore
Singapore, Singapore
4 h-index
12 publications
1.8K citations

Hong Kong Polytechnic University
Hong Kong, Hong Kong
4 h-index
8 publications
436 citations
University of Kalyani
Kalyani, India
4 h-index
6 publications
144 citations
Indian Institute of Management Bangalore
Bengaluru, India
4 h-index
5 publications
105 citations
Indian Institute of Management Lucknow
Lucknow, India
3 h-index
3 publications
57 citations

Université Lumière Lyon 2
Lyon, France
3 h-index
3 publications
227 citations
1–8 of 171
4 publications · 190 citations
4
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3 publications · 343 citations
3
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2 publications · 72 citations
2
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2 publications · 147 citations
2
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Repository · 2 publications · 35 citations
2
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1–5