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1. (CN102609528) Frequent mode association sorting method based on probabilistic graphical model

특허청 : 중국
출원번호: 201210031662.6 출원일: 14.02.2012
공개번호: 102609528 공개일: 25.07.2012
특허번호: 특허부여일: 18.06.2014
공개유형: B
IPC:
G06F 17/30
G SECTION G — 물리학
06
산술논리연산; 계산; 계수
F
전기에 의한 디지털 데이터처리
17
디지털 컴퓨팅 또는 데이터 프로세싱 장비, 방법으로서 특정 기능을 위해 특히 적합한 형태의 것
30
정보검색; 이를 위한 데이터베이스 구조
출원인: Yunnan University
발명자: Liu Weiyi
Yue Kun
대리인: cheng yunbei
우선권 정보
발명의 명칭: (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
요약서: 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。