在进行并行关联规则挖掘时，数据偏斜和工作量平衡这两个数据分布特征影响着剪枝的有效性。
When excavating with parallel association rules, the two data distribution characters, data skewness and workload balance, will affect the validity of pruning.
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.
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。
We proposed the strategy of parallel mining association rules and describe the basic algorithms and analyze the performance of these algorithms.
研究了采用并行算法挖掘关联规则的优化方案。在数据分发（DD）算法的基础上引入了改进的智能数据分发（IDD）算法；
Updated solution in parallel implementation of discovery of association rules was studied, and IDD algorithm based on DD algorithm was introduced.
研究了采用并行算法挖掘关联规则的优化方案。在数据分发（DD）算法的基础上引入了改进的智能数据分发（IDD）算法；
Updated solution in parallel implementation of discovery of association rules was studied, and IDD algorithm based on DD algorithm was introduced.
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