Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Constraint-Based Rule Mining in Large, Dense Databases
Data Mining and Knowledge Discovery
Mining Multiple-Level Association Rules in Large Databases
IEEE Transactions on Knowledge and Data Engineering
A New Algorithm for Faster Mining of Generalized Association Rules
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
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
A New Method for Finding Generalized Frequent Itemsets in Generalized Association Rule Mining
ISCC '02 Proceedings of the Seventh International Symposium on Computers and Communications (ISCC'02)
FP-tax: tree structure based generalized association rule mining
Proceedings of the 9th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Efficient strategies for tough aggregate constraint-based sequential pattern mining
Information Sciences: an International Journal
Discovering significant patterns
Machine Learning
A hybrid artificial immune system and Self Organising Map for network intrusion detection
Information Sciences: an International Journal
Top-down mining of frequent closed patterns from very high dimensional data
Information Sciences: an International Journal
Characterizing network traffic by means of the NetMine framework
Computer Networks: The International Journal of Computer and Telecommunications Networking
An algorithm to mine general association rules from tabular data
Information Sciences: an International Journal
Context-Aware User and Service Profiling by Means of Generalized Association Rules
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part II
Anomaly intrusion detection by clustering transactional audit streams in a host computer
Information Sciences: an International Journal
Discovering multi-label temporal patterns in sequence databases
Information Sciences: an International Journal
Using ontologies to facilitate post-processing of association rules by domain experts
Information Sciences: an International Journal
Mining and filtering multi-level spatial association rules with ARES
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
FARP: Mining fuzzy association rules from a probabilistic quantitative database
Information Sciences: an International Journal
Software defect prediction using relational association rule mining
Information Sciences: an International Journal
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Generalized association rule extraction is a powerful tool to discover a high level view of the interesting patterns hidden in the analyzed data. However, since the patterns are extracted at any level of abstraction, the mined rule set may be too large to be effectively exploited in the decision making process. Thus, to discover valuable and interesting knowledge a post-processing step is usually required. This paper presents the CoGAR framework to efficiently support constrained generalized association rule mining. The generalization process of CoGAR exploits a (user-provided) multiple-taxonomy to drive an opportunistic itemset generalization process, which prevents discarding relevant but infrequent knowledge by aggregating features at different granularity levels. Besides the traditional support and confidence constraints, two further constraints are enforced: (i) schema constraints and (ii) the opportunistic confidence constraint. Schema constraints allow the analyst to specify the structure of the patterns of interest and drive the itemset mining phase. The opportunistic confidence constraint, a new constraint proposed in this paper, allows us to discriminate between significant and redundant rules by analyzing similar rules belonging to different abstraction levels. This constraint is enforced during the rule generation step. Experiments performed on real datasets collected in two different application domains show the effectiveness and the efficiency of the proposed framework in mining constrained generalized association rules.