Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Can we push more constraints into frequent pattern mining?
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
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
An Efficient Data Mining Technique for Discovering Interesting Association Rules
DEXA '97 Proceedings of the 8th International Workshop on Database and Expert Systems Applications
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It is very time-consuming to discover association rules from the mass of data, but not all the rules are interesting to the user, a lot of irrelevant information to the user's requirements may be generated when traditional mining methods are applied. In addition, most of the existing algorithms are for discovering one-dimensional association rules. Therefore, this paper defines a mining language which allows users to specify items of interest to the association rules, as well as the criteria (for example, support, confidence, etc.), and proposes a method based on rough set theory for multidimensional association rule mining methods, dynamically generate frequent item sets and multi-dimensional association rules, which can reduce the search space to generate frequent itemsets. Finally, an example is used to illustrate the algorithm and verify its feasibility and effectiveness.