[1]陆 楠,周春光.互连性层次聚类法在交易数据聚类分析中的应用[J].深圳大学学报理工版,2003,20(1):63-69.
 LU Nan and ZHOU Chun-guang.The Application of Clustering Analysis to Transactional Data-set with Interconnecting Cluster Method[J].Journal of Shenzhen University Science and Engineering,2003,20(1):63-69.
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互连性层次聚类法在交易数据聚类分析中的应用()
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《深圳大学学报理工版》[ISSN:1000-2618/CN:44-1401/N]

卷:
第20卷
期数:
2003年1期
页码:
63-69
栏目:
出版日期:
2003-03-30

文章信息/Info

Title:
The Application of Clustering Analysis to Transactional Data-set with Interconnecting Cluster Method
文章编号:
1000-2618(2002)04-0063-07
作者:
陆 楠1 周春光2
1. 深圳大学信息工程学院, 深圳 518060;
2. 吉林大学计算机科学与技术学院, 长春130021
Author(s):
LU Nan1 and ZHOU Chun-guang2
1. College of Information Engineering Shenzhen University, Shenzhen 518060, P .R .China;
2. Department of Computer Science Jilin University, Changchun 130023, P .R .China
关键词:
数据挖掘交易数据聚类分析相似度
Keywords:
data mining trade data clustering analysis similar degree
分类号:
TP 391
文献标志码:
A
摘要:
提出一种使用互连性度量聚类间相似度的层次聚类算法,并对算法中较为耗时的两步进行了修改,在不牺牲质量的前提下,提高了算法的运行速度.通过分析交易数据的实际聚类,可得到合理的市场分段,预测顾客购买行为.实验结果表明,该方法具有良好的挖掘效果.
Abstract:
A clustering analysis algorithm in transactional data-set to use interconnecting cluster is presented. And it is modified for two step of more time consuming. In precondition of ensuring quality, the running rate is improved. So, it gains reasonable market subsection and forecasts purchasing of customers, by clustering analysis to transactional data-set practically. Experimental result making clear, the algorithm has nicer effect clustering.

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更新日期/Last Update: 2015-12-11