• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

J4 ›› 2008, Vol. 30 ›› Issue (2): 92-95.

• 论文 • 上一篇    下一篇

电子商务系统的数据挖掘与智能推荐预测的研究

刘丽霞[1,2] 庄奕琪[1]   

  • 出版日期:2008-02-01 发布日期:2010-05-19

  • Online:2008-02-01 Published:2010-05-19

摘要:

本文对数据挖掘的概念做了简要的描述,并对决策树、关联规则和聚类三种数据挖掘算法做了分析比较,认为决策树算法虽然对于每一个项有更详细的模式且支持连续的输入,但不能扩展为大的目录;关联规则算法虽然快速、可伸缩,但是对算法的参数非常敏感;聚类算法虽然按相似性对数据进行分组,但是要设置复杂的参数和变量。

关键词: 数据挖掘 聚类 Servlet Socket JDBC 推荐 预测

Abstract:

A brief introduction to the concept of data mining is given and three algorithms are analyzed and compared, including a decision tree algorithm which has the more detailed patterns for each item and allows continuous inputs but does not expand into bigger directories,an association rule algorithm which can be fast and expandable but very sensitive to the parameters, and a clustering algorithm which can group the data according to comparability but needs to set the complex parameters and variables.

Key words: data mining, clustering, Servlet, Socket, JDBC, recommendation, prediction