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1. (CN102609528) 基于概率图模型的频繁模式关联分类方法

专利局 : 中国
申请号: 201210031662.6 申请日: 14.02.2012
公布号: 102609528 公布日: 25.07.2012
授权号: 授权日: 18.06.2014
公布类型: B
国际专利分类:
G06F 17/30
G PHYSICS
06
计算;推算;计数
F
电数字数据处理
17
特别适用于特定功能的数字计算设备或数据处理设备或数据处理方法
30
信息检索;及其数据库结构
申请人: Yunnan University
发明人: Liu Weiyi
Yue Kun
代理人: cheng yunbei
优先权数据:
标题: (EN) Frequent mode association sorting method based on probabilistic graphical model
(ZH) 基于概率图模型的频繁模式关联分类方法
摘要: front page image
(EN) The invention relates to a frequent mode association sorting method based on a probabilistic graphical model, and provides a frequent mode mutual relation representation and frequent mode association sorting method based on the probabilistic graphical model based on execution results of Apriori frequent pattern mining algorithms. A Markov network which is an important probabilistic graphical model is used as a basic framework for knowledge representation to set up internal relation between the frequent mode and the probabilistic graphical model and build the Markov network included in the frequent mode, the frequent mode is subjected to association sorting of different abstract hierarchies by node aggregations from bottom to top, mutual relations of any forms among the frequent modes can be conveniently and efficiently expressed in the global view, better flexibility of association sorting of the users in different abstract hierarchies is achieved, and theoretical basis and technical foundation are provided for subsequent development.
(ZH)

本发明涉及一种基于概率图模型的频繁模式关联分类方法。在Apriori频繁模式挖掘算法的执行结果之上,提供一种基于概率图模型的频繁模式间相互关系的表示及频繁模式的关联分类方法。以马尔可夫网这一重要概率图模型作为知识表示的基本框架,建立频繁模式与概率图模型的内在联系,构建频繁模式中蕴含的马尔可夫网,通过结点自底向上的聚集对频繁模式进行不同抽象层次上的关联分类,可以从全局的角度方便高效地表示频繁模式间任意形式的相互关系,不同抽象层次用户的关联分类具有较好的伸缩性,为后续研发提供理论依据和技术基础。