Generating non-redundant association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Depth first generation of long patterns
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Parallel Mining of Association Rules
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
T-Trees, Vertical Partitioning and Distributed Association Rule Mining
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Tree Structures for Mining Association Rules
Data Mining and Knowledge Discovery
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
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A key process in association rules mining, which has attracted a lot of interest during the last decade, is the discovery of frequent sets of items in a database of transactions. A number of sequential algorithms have been proposed that accomplish this task. On the other hand, only few parallel algorithms have appeared in the literature. In this paper, we study the parallelization of the partial-support-tree approach Goulbourne et al. (2000). Numerical results show that this method is generally competitive, while it is particularly adequate for certain types of datasets.