Weighted fuzzy pattern matching
Fuzzy Sets and Systems - Mathematical Modelling
An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
What are fuzzy rules and how to use them
Fuzzy Sets and Systems - Special issue dedicated to the memory of Professor Arnold Kaufmann
Association rules over interval data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Mining fuzzy association rules in databases
ACM SIGMOD Record
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Fuzzy Functional Dependencies and Fuzzy Association Rules
DaWaK '99 Proceedings of the First International Conference on Data Warehousing and Knowledge Discovery
Mining Fuzzy Quantitative Association Rules
ICTAI '99 Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence
A Fuzzy Approach for Mining Quantitative Association Rules
A Fuzzy Approach for Mining Quantitative Association Rules
Mining Weighted Association Rules for Fuzzy Quantitative Items
Mining Weighted Association Rules for Fuzzy Quantitative Items
Learning fuzzy rules with their implication operators
Data & Knowledge Engineering
Elicitation of fuzzy association rules from positive and negative examples
Fuzzy Sets and Systems
A discussion of indices for the evaluation of fuzzy associations in relational databases
IFSA'03 Proceedings of the 10th international fuzzy systems association World Congress conference on Fuzzy sets and systems
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Fuzzy association rules provide a data mining tool which is especially interesting from a knowledge-representational point of view since fuzzy attribute values allow for expressing rules in terms of natural language. So far, however, association rules of this type have not been investigated thoroughly from a semantical point of view. Particularly, the quality measures which have been proposed for assessing such rules are mostly "ad-hoc" generalizations of measures for classical rules. In this paper, we show that fuzzy association rules can be interpreted in different ways and that the interpretation has a strong influence on their assessment and, hence, on the process of rule mining. We motivate the use of multiple-valued implication operators in order to model fuzzy association rules and propose quality measures suitable for this type of rule.