Depth-first iterative-deepening: an optimal admissible tree search
Artificial Intelligence
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Mining the most interesting rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Knowledge Discovery in Databases
Knowledge Discovery in Databases
Scalable Techniques for Mining Causal Structures
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
Constraint-Based Rule Mining in Large, Dense Databases
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Efficient mining of statistical dependencies
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
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Mining transaction data by extracting rules to express relationships between itemsets is a classical form of data mining. The rule evaluation method used dictates the nature and the strength of the relationship, eg. an association, a correlation, a dependency, etc. The widely used Apriori algorithm employs breadth-first search to find frequent and confident association rules. The Multi-Stream Dependency Detection (MSDD) algorithm uses iterative deepening (ID) to discover dependency structures. The search bound for ID can be based on various characteristics of the search space, such as a change in the tree depth (MSDD), or a change in the quality of explored states. This paper proposes an ID-based algorithm, IDGmax, whose search bound is based on a desired quality of the discovered rules. The paper also compares strategies to relax the search bound and shows that the choice of this relaxation strategy can significantly speed up the search which can explore all possible rules.