计算机应用 ›› 2012, Vol. 32 ›› Issue (01): 196-198.DOI: 10.3724/SP.J.1087.2012.00196

• 数据库技术 • 上一篇    下一篇

动态关联规则的趋势度挖掘方法

张忠林,曾庆飞,许凡   

  1. 兰州交通大学 电子与信息工程学院,兰州 730070
  • 收稿日期:2011-07-15 修回日期:2011-09-23 发布日期:2012-02-06 出版日期:2012-01-01
  • 通讯作者: 张忠林
  • 作者简介:张忠林(1965-),男,河北阜城人,教授,博士,CCF会员,主要研究方向:智能信息处理、软件工程;曾庆飞(1985-),男,山东青岛人,硕士研究生,主要研究方向:智能信息处理;许凡(1987-),男,湖北仙桃人,硕士研究生,主要研究方向:智能信息处理。
  • 基金资助:

    国家自然科学基金资助项目(61163010);甘肃省科技支撑计划项目(1011GKCA040)

Method of data tendency measure mining in dynamic association rules

ZHANG Zhong-lin,ZENG Qing-fei,XU Fan   

  1. School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou Gansu 730070, China
  • Received:2011-07-15 Revised:2011-09-23 Online:2012-02-06 Published:2012-01-01
  • Contact: ZHANG Zhong-lin

摘要: 针对规则随着时间变化的特点,在分析原有定义和对支持度向量(SV)和置信度向量分类的基础上,提出了动态关联规则趋势度的挖掘方法。首先,利用趋势度阈值消除无价值的规则,减小候选项集;其次,产生动态关联规则的趋势度元规则,找出具有价值的规则,提高挖掘质量;最后,通过对具有增减和周期趋势的事物数据库分析,证明了所提方法的有效性。

关键词: 数据挖掘, 动态关联规则, 趋势度, 元关联规则

Abstract: Based on the original definition and classification of Support Vector (SV) and confidence vector, this paper put forward a method of data tendency measure mining in dynamic association rules, according to the characteristic of rules with time changing. First, taking advantage of tendency measure threshold to eliminate useless rules, the item sets candidates can be reduced. Second, producing the dynamic association rule, this method found out valuable rules and improved the mining quality. Finally, by analyzing a transaction database that is characterized by the tendency of changes and cycles, the analytical results verify the validity of the proposed method.

Key words: data mining, dynamic association rule, tendency measure, meta-association rule

中图分类号: