There were problems in traditional parallel algorithms for mining frequent itemsets more or less: data deviation, large scale communication, frequent synchronization and scanning database.
传统的挖掘频繁项集的并行算法存在数据偏移、通信量大、同步次数较多和扫描数据库次数较多等问题。
We proposed the strategy of parallel mining association rules and describe the basic algorithms and analyze the performance of these algorithms.
提出了关联规则的并行挖掘策略并且对相应的并行算法进行了性能分析。
The current algorithms of parallel association rules mining are analyzed, and the main factors affecting the performance of the mining algorithm in the multi-processor system are discussed.
文章在分析已有并行关联规则挖掘算法的基础上，讨论了多处理器系统中影响并行关联规则挖掘算法性能的主要问题。
The current algorithms of parallel association rules mining are analyzed, and the main factors affecting the performance of the mining algorithm in the multi-processor system are discussed.
文章在分析已有并行关联规则挖掘算法的基础上，讨论了多处理器系统中影响并行关联规则挖掘算法性能的主要问题。
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