Fuzzy entropy and conditioning
Information Sciences: an International Journal
Fuzzification of set inclusion: theory and applications
Fuzzy Sets and Systems
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Fuzzy Sets and Systems
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Simple association rules (SAR) and the SAR-based rule discovery
Computers and Industrial Engineering
Mining Both Positive and Negative Association Rules
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Mining for Strong Negative Associations in a Large Database of Customer Transactions
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
Fuzzy Association Rules: Semantic Issues and Quality Measures
Proceedings of the International Conference, 7th Fuzzy Days on Computational Intelligence, Theory and Applications
Selecting the right interestingness measure for association patterns
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Sinha-Dougherty approach to the fuzzification of set inclusion revisited
Fuzzy Sets and Systems - Implication operators
A note on quality measures for fuzzy association rules
IFSA'03 Proceedings of the 10th international fuzzy systems association World Congress conference on Fuzzy sets and systems
Association rule mining: models and algorithms
Association rule mining: models and algorithms
A proposed method for learning rule weights in fuzzy rule-based classification systems
Fuzzy Sets and Systems
Fuzzy Sets and Systems
Fuzzy correlation rules mining
ACOS'07 Proceedings of the 6th Conference on WSEAS International Conference on Applied Computer Science - Volume 6
Mining Both Positive and Negative Association Rules from Frequent and Infrequent Itemsets
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
A fuzzy statistics based method for mining fuzzy correlation rules
WSEAS Transactions on Mathematics
Customer pattern search for after-sales service in manufacturing
Expert Systems with Applications: An International Journal
Visualizing and fuzzy filtering for discovering temporal trajectories of association rules
Journal of Computer and System Sciences
Fuzzy sets in database and information systems: Status and opportunities
Fuzzy Sets and Systems
Proceedings of the 20th international conference companion on World wide web
Perception based time series data mining with MAP transform
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
Discovering fuzzy association rules with interest and conviction measures
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part IV
A formal model for mining fuzzy rules using the RL representation theory
Information Sciences: an International Journal
FAR-miner: a fast and efficient algorithm for fuzzy association rule mining
International Journal of Business Intelligence and Data Mining
A hierarchical approach to multi-class fuzzy classifiers
Expert Systems with Applications: An International Journal
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The aim of this paper is to provide a crystal clear insight into the true semantics of the measures of support and confidence that are used to assess rule quality in fuzzy association rule mining. To achieve this, we rely on two important pillars: the identification of transactions in a database as positive or negative examples of a given association between attributes, and the correspondence between measures of support and confidence on one hand, and measures of compatibility and inclusion on the other hand. In this way we remove the ''mystery'' from recently suggested quality measures for fuzzy association rules.