Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 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
A New Algorithm for Faster Mining of Generalized Association Rules
PKDD '98 Proceedings of the Second European Symposium on Principles of 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
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th 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
Improving Case-Based Recommendation: A Collaborative Filtering Approach
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Data Mining of Association Rules and the Process of Knowledge Discovery in Databases
Industrial Conference on Data Mining: Advances in Data Mining, Applications in E-Commerce, Medicine, and Knowledge Management
Data Mining Support for Case-Based Collaborative Recommendation
AICS '02 Proceedings of the 13th Irish International Conference on Artificial Intelligence and Cognitive Science
Automated error detection using association rules
Intelligent Data Analysis
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Since the introduction of association rules, many algorithms have been developed to perform the computationally very intensive task of association rule mining. During recent years there has been the tendency in research to concentrate on developing algorithms for specialized tasks, e.g. for mining optimized rules or incrementally updating rule sets. Here we return to the "classic" problem, namely the efficient generation of all association rules that exist in a given set of transactions with respect to minimum support and minimum confidence. From our point of view, the performance problem concerning this task is still not adequately solved. In this paper we address two topics: First of all, today there is no satisfying comparison of the common algorithms. Therefore we identify the fundamental strategies of association rule mining and present a general framework that is independent of any particular approach and its implementation. Based on this we carefully analyze the algorithms. We explain differences and similarities in performance behavior and complete our theoretic insights by runtime experiments. Second, the results are quite surprising and enable us to derive a new algorithm. This approach avoids the identified pitfalls and at the same time profits from the strengths of known approaches. It turns out that it achieves remarkably better runtimes than the previous algorithms.