摘要
针对事务库发生变化后关联规则的更新问题,讨论了一种只对具有实用价值的关联规则更新其前件的方法.首先分析了关联规则各组件间的依赖关系及其不确定性,进而建立描述其中所蕴含不确定性知识的贝叶斯网模型(称为规则贝叶斯网),并提出了基于Gibbs采样的规则贝叶斯网近似推理算法,从而实现关联规则的更新.实验结果表明,作者提出的基于概率图模型的关联规则前件更新方法具有高效性和可行性.
Aiming at the update of association rules with respect to the changes of the transaction database,in this paper,we discussed an approach for updating the former components of valuable association rules.First,we analyzed the dependency relationships and uncertainty among the components in association rules.Then,we constructed the Bayesian network（BN） model,called rule BN,to represent this uncertain knowledge.As well,we proposed an algorithm for RBN＇s approximate reasoning based on Gibbs sampling,so that the update of association rules can be fulfilled.Experimental results show that our proposed method for updating the antecedents of association rules is feasible efficient.
出处
《云南大学学报（自然科学版）》
CAS
CSCD
北大核心
2013年第2期155-161,共7页
Journal of Yunnan University(Natural Sciences Edition)
基金
国家自然科学基金项目(61063009
61163003)
国家教育部博士点基金新教师类课题(20105301120001)
云南省中青年学术技术带头人后备人才培养项目(2012HB004)
云南省教育厅科研基金项目(2011Z015
2011Y500)
关键词
关联规则更新
不确定性
贝叶斯网
GIBBS采样
概率推理
association rule update
uncertainty
Bayesian network
Gibbs sampling
probabilistic reasoning
