Fuzzy expert systems
Distributed representation of fuzzy rules and its application to pattern classification
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
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
Mining optimized association rules for numeric attributes
PODS '96 Proceedings of the fifteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Discovering data mining: from concept to implementation
Discovering data mining: from concept to implementation
Data mining: concepts and techniques
Data mining: concepts and techniques
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovery of Multiple-Level Association Rules from Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Web Mining: Information and Pattern Discovery on the World Wide Web
ICTAI '97 Proceedings of the 9th International Conference on Tools with Artificial Intelligence
Fuzzy data mining for interesting generalized association rules
Fuzzy Sets and Systems - Theme: Learning and modeling
An Efficient Algorithm for Incremental Mining of Association Rules
RIDE '05 Proceedings of the 15th International Workshop on Research Issues in Data Engineering: Stream Data Mining and Applications
Short communication: Uncertainty measures for fuzzy relations and their applications
Applied Soft Computing
Predicting uncertain behavior of industrial system using FM-A practical case
Applied Soft Computing
CBAR: an efficient method for mining association rules
Knowledge-Based Systems
Selecting fuzzy if-then rules for classification problems using genetic algorithms
IEEE Transactions on Fuzzy Systems
A fuzzy coherent rule mining algorithm
Applied Soft Computing
Fuzzy association rule mining approaches for enhancing prediction performance
Expert Systems with Applications: An International Journal
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During electronic commerce (EC) environment, how to effectively mine the useful transaction information will be an important issue to be addressed in designing the marketing strategy for most enterprises. Especially, the relationships between different databases (e.g., the transaction and online browsing database) may have the unknown and potential knowledge of business intelligence. Two important issues of mining association rules were mentioned to address EC application in this study. The first issue is the discovery of generalized fuzzy association rules in the transaction database. The second issue is to discover association rules from the web usage data and the large itemsets identified in the transaction database. A cluster-based fuzzy association rules (CBFAR) mining architecture is then proposed to simultaneously address such two issues in this study. Three contributions were achieved as: (a) an efficient fuzzy association rule miner based on cluster-based fuzzy-sets tables is presented to identify all the large fuzzy itemsets; (b) this approach requires less contrast to generate large itemsets; (3) a fuzzy rule mining approach is used to compute the confidence values for discovering the relationships between transaction database and browsing information database. Finally, a simulated example during EC environment is provided to demonstrate the rationality and feasibility of the proposed approach.