Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Expert-Driven Validation of Rule-Based User Models in Personalization Applications
Data Mining and Knowledge Discovery
Fuzzy association rules and the extended mining algorithms
Information Sciences—Informatics and Computer Science: An International Journal
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
Mining Association Rules with Weighted Items
IDEAS '98 Proceedings of the 1998 International Symposium on Database Engineering & Applications
Fuzzy data mining for interesting generalized association rules
Fuzzy Sets and Systems - Theme: Learning and modeling
A Cluster-Based Method for Mining Generalized Fuzzy Association Rules
ICICIC '06 Proceedings of the First International Conference on Innovative Computing, Information and Control - Volume 2
Multi-level fuzzy mining with multiple minimum supports
Expert Systems with Applications: An International Journal
Generalized association rule mining using an efficient data structure
Expert Systems with Applications: An International Journal
Multi-level Fuzzy Association Rules Mining via Determining Minimum Supports and Membership Functions
ISMS '11 Proceedings of the 2011 Second International Conference on Intelligent Systems, Modelling and Simulation
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The most common use of fuzzy taxonomies in mining generalized association rules occurs in the pre-processing stage, through the concept of extended transaction. A related problem is that extended transactions lead to the generation of huge amount of candidates and rules. Beyond that, the inclusion of ancestors may to generate redundancy problems. Besides, it is possible to see that the works have only assumed the total relation between database items and taxonomy nodes. The total relation occurs when all structure items have an equivalent representative item in the dataset, and vice-versa. Furthermore, the works have been directing for the question of mining fuzzy rules, exploring linguistic terms, but few approaches have explored new steps of the mining process. In this sense, this paper proposes the extended FOntGAR algorithm, an algorithm for mining generalized association rules under all levels of fuzzy ontologies, where the relation between database items and ontology items do not need be total. In this work the generalization is done during the post-processing step.