A formal model for mining fuzzy rules using the RL representation theory

DOI10.1016/j.ins.2011.07.029zbMath1239.68061OpenAlexW2084410669MaRDI QIDQ424771

J. Herrera, H. S. Yoon

Publication date: 4 June 2012

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2011.07.029

zbMATH Keywords

data miningabsence of itemsfuzzy association rulesGUHA modelnegative rulesrestriction levels

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (6)

Information fusion from multiple databases using meta-association rules ⋮ RL-instances: an alternative to conjunctive fuzzy sets of tuples for flexible querying in relational databases ⋮ On Fuzzy Modus Ponens to Assess Fuzzy Association Rules ⋮ Spark solutions for discovering fuzzy association rules in big data ⋮ Representation by levels: an alternative to fuzzy sets for fuzzy data mining ⋮ Fuzzy four-fold tables: their properties and use in fuzzy association analysis

Uses Software

• UCI-ml

Cites Work

• Mining web documents to find additional query terms using fuzzy association rules
• Examples, counterexamples, and measuring fuzzy associations
• Elicitation of fuzzy association rules from positive and negative examples
• Gradual elements in a fuzzy set
• The GUHA method of automatic hypotheses determination
• Logic of association rules
• NEW APPROACHES FOR DISCOVERING EXCEPTION AND ANOMALOUS RULES
• STUDYING INTEREST MEASURES FOR ASSOCIATION RULES THROUGH A LOGICAL MODEL
• Fuzzy sets
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item

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