An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Fast discovery of association rules
Advances in knowledge discovery and data mining
Query flocks: a generalization of association-rule mining
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Using a knowledge cache for interactive discovery of association rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovery of frequent DATALOG patterns
Data Mining and Knowledge Discovery
MSQL: A Query Language for Database Mining
Data Mining and Knowledge Discovery
Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Materialized Data Mining Views
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Incremental Refinement of Mining Queries
DaWaK '99 Proceedings of the First International Conference on Data Warehousing and Knowledge Discovery
Optimal implementation of conjunctive queries in relational data bases
STOC '77 Proceedings of the ninth annual ACM symposium on Theory of computing
Using Condensed Representations for Interactive Association Rule Mining
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Mining all frequent projection-selection queries from a relational table
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Mining frequent conjunctive queries in star schemas
IDEAS '09 Proceedings of the 2009 International Database Engineering & Applications Symposium
Computing Supports of Conjunctive Queries on Relational Tables with Functional Dependencies
Fundamenta Informaticae
Discovery and application of functional dependencies in conjunctive query mining
DaWaK'10 Proceedings of the 12th international conference on Data warehousing and knowledge discovery
An efficient computation of frequent queries in a star schema
DEXA'10 Proceedings of the 21st international conference on Database and expert systems applications: Part II
Extracting semantics in OLAP databases using emerging cubes
Information Sciences: an International Journal
A relational view of pattern discovery
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications - Volume Part I
Computation of mining queries: an algebraic approach
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
Towards mining frequent queries in star schemes
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
Mining frequent conjunctive queries using functional and inclusion dependencies
The VLDB Journal — The International Journal on Very Large Data Bases
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Association rule mining often requires the repeated execution of some extraction algorithm for different values of the support and confidence thresholds, as well as for different source datasets. This is an expensive process, even if we use the best existing algorithms. Hence the need for incremental mining, whereby mining results already obtained can be used to accelerate subsequent steps in the mining process.In this paper, we present an approach for the incremental mining of multi-dimensional association rules. In our approach, association rule mining takes place in a mining context which specifies the form of rules to be mined. Incremental mining is obtained by combining mining contexts using relational algebra operations.