| 一种自适应快速关联规则挖掘算法 |
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| 引用本文： | 张海英,浦磊,潘永湘.一种自适应快速关联规则挖掘算法[J].西安理工大学学报,2004,20(4):404-407. |
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| 作者姓名： | 张海英 浦磊 潘永湘 |
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| 作者单位： | 西安理工大学,自动化与信息工程学院,陕西,西安,710048 |
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| 摘 要： | 提出一种从大型数据库中挖掘关联规则的快速算法——自适应快速关联规则提取算法。该算法以经典的Apriori算法及其他各种优化算法为基础，引入了自适应步长和扫描树的概念，并采用修剪法对Apriori算法进行了改进。理论分析与实验结果表明，该算法比Apriori算法的应用效率高，同时也证实了其有效性。
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| 关 键 词： | 数据挖掘 关联规则 自适应步长 树修剪 |
| 文章编号： | 1006-4710(2004)04-0404-04 |
| 修稿时间： | 2003年5月21日 |
A Self-Adapted Fast Data Mining Algorithm for Association Rules |
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| ZHANG Hai-ying,PU Lei,PAN Yong-xiang.A Self-Adapted Fast Data Mining Algorithm for Association Rules[J].Journal of Xi'an University of Technology,2004,20(4):404-407. |
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| Authors: | ZHANG Hai-ying PU Lei PAN Yong-xiang |
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| Abstract: | This paper suggests a fast algorithm for data mining association rules in large database.Based on the traditional Apriori and other optimal algorithms,the concept of self-adapted step and scanning tree is introduced.The pruning method is adopted to improve Apriori algorithm.The theoretical analysis and experiment results indicate that this algorithm is of higher application efficiency than Apriori algorithm,and that its effetiveness is also proved. |
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| Keywords: | data mining association rules self-adapted step tree pruning |
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