Random sampling with a reservoir
ACM Transactions on Mathematical Software (TOMS)
Randomized algorithms
A localized algorithm for parallel association mining
Proceedings of the ninth annual ACM symposium on Parallel algorithms and architectures
Effect of Data Distribution in Parallel Mining of Associations
Data Mining and Knowledge Discovery
Parallel Mining of Association Rules
IEEE Transactions on Knowledge and Data Engineering
Effect of Data Skewness and Workload Balance in Parallel Data Mining
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
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
New Parallel Algorithms for Frequent Itemset Mining in Very Large Databases
SBAC-PAD '03 Proceedings of the 15th Symposium on Computer Architecture and High Performance Computing
Frequent Pattern Mining on Message Passing Multiprocessor Systems
Distributed and Parallel Databases
Toward more parallel frequent itemset mining algorithms
PDCS '07 Proceedings of the 19th IASTED International Conference on Parallel and Distributed Computing and Systems
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In this paper, we present a novel method for parallelization of an arbitrary depth-first search (DFS in short) algorithm for mining of all FIs. The method is based on the so called reservoir sampling algorithm. The reservoir sampling algorithm in combination with an arbitrary DFS mining algorithm executed on a database sample takes an uniformly but not independently distributed sample of all FIs using the reservoir sampling. The sample is then used for static load-balancing of the computational load of a DFS algorithm for mining of all FIs.