Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
A database perspective on knowledge discovery
Communications of the ACM
Query flocks: a generalization of association-rule mining
SIGMOD '98 Proceedings of the 1998 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
Optimization of constrained frequent set queries with 2-variable constraints
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
ACM Transactions on Information Systems (TOIS)
Exploiting succinct constraints using FP-trees
ACM SIGKDD Explorations Newsletter
Discovery in multi-attribute data with user-defined constraints
ACM SIGKDD Explorations Newsletter
Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
On Dual Mining: From Patterns to Circumstances, and Back
Proceedings of the 17th International Conference on Data Engineering
Computing Iceberg Queries Efficiently
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Performance Issues in Incremental Warehouse Maintenance
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th 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
Incremental Refinement of Mining Queries
DaWaK '99 Proceedings of the First International Conference on Data Warehousing and Knowledge Discovery
A General Incremental Technique for Maintaining Discovered Association Rules
Proceedings of the Fifth International Conference on Database Systems for Advanced Applications (DASFAA)
A perspective on inductive databases
ACM SIGKDD Explorations Newsletter
DualMiner: a dual-pruning algorithm for itemsets with constraints
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Querying multiple sets of discovered rules
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Constraint-Based Rule Mining in Large, Dense Databases
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Efficient Evaluation of Queries with Mining Predicates
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Optimization of association rule mining queries
Intelligent Data Analysis
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Constraints-based mining languages are widely exploited to enhance the KDD process. In this paper we propose a novel incremental approach to extract itemsets and association rules from large databases. Here incremental is used to emphasize that the mining engine does not start from scratch. Instead, it exploits the result set of previously executed queries in order to simplify the mining process. Incremental algorithms show several beneficial features. First of all they exploit previous results in the pruning of the itemset lattice. Second, they are able to exploit the mining constraints of the current query in order to prune the search space even more. In this paper we propose two incremental algorithms that are able to deal with two — recently identified — types of constraints, namely item dependent and context dependent ones. Moreover, we describe an algorithm that can be used to extract association rules from scratch in presence of context dependent constraints.