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
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Exploiting succinct constraints using FP-trees
ACM SIGKDD Explorations Newsletter
Discovery in multi-attribute data with user-defined constraints
ACM SIGKDD Explorations Newsletter
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
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
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
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
Answering constraint-based mining queries on itemsets using previous materialized results
Journal of Intelligent Information Systems
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In recent years, the KDD process has been advocated to be an iterative and interactive process. It is seldom the case that a user is able to answer immediately with a single query all his questions on data. On the contrary, the workflow of the typical user consists in several steps, in which he/she iteratively refines the extracted knowledge by inspecting previous results and posing new queries. Given this view of the KDD process, it becomes crucial to have KDD systems that are able to exploit past results thus minimizing computational effort. This is expecially true in environments in which the system knowledge base is the result of many discoveries on data made separately by the collaborative effort of different users. In this paper, we consider the problem of mining frequent association rules from database relations. We model a general, constraint-based, mining language for this task and study its properties w.r.t. the problem of re-using past results. In particular, we individuate two class of query constraints, namely “item dependent” and “context dependent” ones, and show that the latter are more difficult than the former ones. Then, we propose two newly developed algorithms which allow the exploitation of past results in the two cases. Finally, we show that the approach is both effective and viable by experimenting on some datasets.