Fast discovery of association rules
Advances in knowledge discovery and data mining
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Clustering transactions using large items
Proceedings of the eighth international conference on Information and knowledge management
Levelwise Search and Borders of Theories in KnowledgeDiscovery
Data Mining and Knowledge Discovery
Free-Sets: A Condensed Representation of Boolean Data for the Approximation of Frequency Queries
Data Mining and Knowledge Discovery
Discovering Frequent Closed Itemsets for Association Rules
ICDT '99 Proceedings of the 7th International Conference on Database Theory
Mining N-most Interesting Itemsets
ISMIS '00 Proceedings of the 12th International Symposium on Foundations of Intelligent Systems
Pattern Detection and Discovery
Proceedings of the ESF Exploratory Workshop on Pattern Detection and Discovery
A Theory of Inductive Query Answering
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
The Chosen Few: On Identifying Valuable Patterns
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Mining constraint-based patterns using automatic relaxation
Intelligent Data Analysis
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
A bi-clustering framework for categorical data
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Proceedings of the 2004 international conference on Local Pattern Detection
LPD'04 Proceedings of the 2004 international conference on Local Pattern Detection
An efficient framework for mining flexible constraints
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
A survey on condensed representations for frequent sets
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
A relational view of pattern discovery
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications - Volume Part I
Bucket Learning: Improving model quality through enhancing local patterns
Knowledge-Based Systems
Combining CSP and constraint-based mining for pattern discovery
ICCSA'10 Proceedings of the 2010 international conference on Computational Science and Its Applications - Volume Part II
Optimonotone Measures For Optimal Rule Discovery
Computational Intelligence
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It is well known that local patterns are at the core of a lot of knowledge which may be discovered from data. Nevertheless, use of local patterns is limited by their huge number and computational costs. Several approaches (e.g., condensed representations, pattern set discovery) aim at selecting or grouping local patterns to provide a global view of the data. In this paper, we propose the idea of global constraints to write queries addressing global patterns as sets of local patterns. Usefulness of global constraints is to take into account relationships between local patterns, such relations expressing a user bias according to its expectation (e.g., search of exceptions, top-kpatterns). We think that global constraints are a powerful way to get meaningful patterns. We propose the generic Approximate-and-Push approach to mine patterns under global constraints and we give a method for the case of the top-kpatterns w.r.t. any measure. Experiments show its efficiency since it was not feasible to mine such patterns beforehand.