Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
Designing Templates for Mining Association Rules
Journal of Intelligent Information Systems
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
A New SQL-like Operator for Mining Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Composition of Mining Contexts for Efficient Extraction of Association Rules
EDBT '02 Proceedings of the 8th International Conference on Extending Database Technology: Advances in Database Technology
Using Condensed Representations for Interactive Association Rule Mining
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Data Mining as Constraint Logic Programming
Computational Logic: Logic Programming and Beyond, Essays in Honour of Robert A. Kowalski, Part II
Interactive Constraint-Based Sequential Pattern Mining
ADBIS '01 Proceedings of the 5th East European Conference on Advances in Databases and Information Systems
Data Access Paths for Frequent Itemsets Discovery
ADBIS '02 Proceedings of the 6th East European Conference on Advances in Databases and Information Systems
An Algebra for Inductive Query Evaluation
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
A greedy approach to concurrent processing of frequent itemset queries
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
Optimizing a sequence of frequent pattern queries
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
On multiple query optimization in data mining
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
A novel incremental approach to association rules mining in inductive databases
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
Partition-Based approach to processing batches of frequent itemset queries
FQAS'06 Proceedings of the 7th international conference on Flexible Query Answering Systems
A Framework for Synthesizing Arbitrary Boolean Queries Induced by Frequent Itemsets
International Journal of Knowledge-Based Organizations
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A first attempt to extract association rules from a database frequently yields a significant number of rules, which may be rather difficult for the user to browse in searching interesting information. However, powerful languages allow the user to specify complex mining queries to reduce the amount of extracted information. Hence, a suitable rule set may be obtained by means of a progressive refinement of the initial query. To assist the user in the refinement process, we identify several types of containment relationships between mining queries that may lead the process. Since the repeated extraction of a large rule set is computationally expensive, we propose an algorithm to perform an incremental recomputation of the output rule set. This algorithm is based on the detection of containment relationships between mining queries.