Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
A Machine-Oriented Logic Based on the Resolution Principle
Journal of the ACM (JACM)
The Semantics of Predicate Logic as a Programming Language
Journal of the ACM (JACM)
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Depth first generation of long patterns
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
On Dual Mining: From Patterns to Circumstances, and Back
Proceedings of the 17th International Conference on Data Engineering
FARM: A Framework for Exploring Mining Spaces with Multiple Attributes
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
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
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Answering constraint-based mining queries on itemsets using previous materialized results
Journal of Intelligent Information Systems
A framework to support multiple query optimization for complex mining tasks
MDM '05 Proceedings of the 6th international workshop on Multimedia data mining: mining integrated media and complex data
INCREMENTAL EXTRACTION OF ASSOCIATION RULES IN APPLICATIVE DOMAINS
Applied Artificial Intelligence
Using a reinforced concept lattice to incrementally mine association rules from closed itemsets
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
An efficient discovery of class-restricted MARs
AIKED'11 Proceedings of the 10th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases
Optimization of association rules extraction through exploitation of context dependent constraints
AI*IA'05 Proceedings of the 9th conference on Advances in Artificial Intelligence
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
A novel incremental approach to association rules mining in inductive databases
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
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There has been a growing interest in mining frequent itemsets in relational data with multiple attributes. A key step in this approach is to select a set of attributes that group data into transactions and a separate set of attributes that labels data into items. Unsupervised and unrestricted mining, however, is stymied by the combinatorial complexity and the quantity of patterns as the number of attributes grows. In this paper, we focus on leveraging the semantics of the underlying data for mining frequent itemsets. For instance, there are usually taxonomies in the data schema and functional dependencies among the attributes. Domain knowledge and user preferences often have the potential to significantly reduce the exponentially growing mining space. These observations motivate the design of a user-directed data mining framework that allows such domain knowledge to guide the mining process and control the mining strategy. We show examples of tremendous reduction in computation by using domain knowledge in mining relational data with multiple attributes.