| 关联规则发现中的聚类方法 |
| |
| 引用本文： | 谢坤武,陈世强,毕晓玲.关联规则发现中的聚类方法[J].计算机科学,2007,34(8):180-183. |
| |
| 作者姓名： | 谢坤武 陈世强 毕晓玲 |
| |
| 作者单位： | 湖北民族学院信息工程学院,恩施445000 |
| |
| 基金项目： | 国家科技攻关项目
,
湖北省科技攻关项目 |
| |
| 摘 要： | 算法MARC（Mining Association Rules using Clustering）将聚类技术应用到关联规则的发现上,MARC利用聚类技术压缩交易数据库,从而减少开采算法需要处理的数据量以提高开采效率,同时算法提出了聚类汇总转换的概念用以减轻压缩数据带来的信息丢失.在几个实际数据集上的实验表明该算法可以达到高精度和高性能.
|
| 关 键 词： | 数据开采 聚类分析 关联规则 |
Clustering Method for Mining Association Rules |
| |
| XIE Kun-Wu,CHEN Shi-Qiang,BI Xiao-Ling.Clustering Method for Mining Association Rules[J].Computer Science,2007,34(8):180-183. |
| |
| Authors: | XIE Kun-Wu CHEN Shi-Qiang BI Xiao-Ling |
| |
| Affiliation: | School of Information Engineering, Hubei Institute for Nationalities, Enshi 445000 |
| |
| Abstract: | MARC algorithms are proposed to apply clustering analysis to other fields.It integrates clustering into association rules discovery to reduce the size of data sets.It also uses CS(Clustering Summary) transformation to alleviate the loss of information brought by the compression.MARC only needs to scan the database one time.The experiments with severa1 rea1 data sets have demonstrated that MARC can achieve quite well precision and high performance. |
| |
| Keywords: | Data mining Clustering analysis Association rules |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
| 点击此处可从《计算机科学》浏览原始摘要信息 |
|
点击此处可从《计算机科学》下载全文 |
|
