| 关联规则挖掘Apriori算法的研究与改进 |
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| 引用本文： | 谢宗毅. 关联规则挖掘Apriori算法的研究与改进[J]. 杭州电子科技大学学报, 2006, 26(3): 78-82 |
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| 作者姓名： | 谢宗毅 |
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| 作者单位： | 杭州电子科技大学计算机学院,浙江,杭州,310018 |
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| 摘 要： | 关联规则挖掘是数据挖掘领域中的重要研究方向，该文在分析关联规则挖掘Apriori算法原理和性能的基础上，指出了该算法存在着两点不足：扫描事务数据库的次数和连接成高维候选项目集时的比较次数太多。并提出了一种效率更高的S-Apriofi算法，该算法通过采用新的数据结构和原理，克服了传统Apriori算法的缺点，从而大大提高了运算效率。
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| 关 键 词： | 关联规则 频繁项目集 支持度 事务向量 |
| 文章编号： | 1001-9146（2006）03-0078-05 |
| 收稿时间： | 2006-03-21 |
| 修稿时间： | 2006-03-21 |
Research and Improvement of Apriori Algorithm for Mining Association Rules |
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| XIE Zong-yi. Research and Improvement of Apriori Algorithm for Mining Association Rules[J]. Journal of Hangzhou Dianzi University, 2006, 26(3): 78-82 |
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| Authors: | XIE Zong-yi |
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| Affiliation: | School of Compeer Science, Hangzhou Dianzi University, Hangzhou Zhejiang 310018, China |
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| Abstract: | The mining association rules is an important aspect in the data mining research field.Based on analyzing theory and performance of Apriori algorithm for mining association rules,this paper expounded the two defects of Apriori algorithm : the times of scaning database and compareing condition for joining the higher degree frequent itemsets are overmany,and proposed high efficient S_Aprior algorithm.The improved algorithm overcomes the shortcoming of the traditional Apriori algorithm and can greatly improves operational efficiency through adoption of new database structure and principle. |
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| Keywords: | association rules frequent itemsets support degree task vector |
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