| 摘要: |
| 经典的关联规则求解算法(如Apriori算法)是串行算法，当数据量比较大时挖掘效率较低；提出了新的并行BVP算法，BVP算法通过多线程并行读取数据并计算相应的数据特征，然后计算频繁项集和关联规则；实验结果表明：相对于经典Apriori算法，算法执行效率更高。 |
| 关键词: 数据挖掘；关联规则；并行 频繁项集 |
| DOI： |
| 分类号: |
| 基金项目: |
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| An Improved Rule Association Parallel Mining Algorithm |
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WANG Feng kun，ZHANG Ting ting
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| Abstract: |
| The classical algorithm of association rules (such as Apriori algorithm) is a serial algorithm, when the data volume is relatively large, mining efficiency is low. A new parallel BVP algorithm is proposed. The BVP algorithm reads data from multiple threads and calculates the corresponding data. Then, the frequent itemsets and association rules are computed. Experimental results show that the algorithm is more efficient than the classical Apriori algorithm. |
| Key words: data mining association rules parallel frequent itemsets |
