具有属性析取萎缩-扩张特征的动态数据智能挖掘  

Dynamic Data Intelligent Mining with Attributes Disjunctive Reduction and Expansion Characteristics

作　　者：徐凤生[1] 闫立梅[1] 史开泉[2] 

机构地区：[1]德州学院数学科学学院,德州253023 [2]山东大学数学与系统科学学院,济南250100

出　　处：《计算机科学》2015年第5期215-220,共6页Computer Science

基　　金：山东省高校科技计划(J12LN92)项目资助

摘　　要：S-粗集(singular rough sets)是把动态特征引入到Z.Pawlak粗集中对其加以改进而提出的,S-粗集具有动态特征。S-粗集具有3种形式:单向S-粗集(one direction singular rough sets)、单向S-粗集对偶(dual of one direction singular rough sets)与双向S-粗集(two direction singular rough sets);在一定条件下,单向S-粗集、单向S-粗集对偶与双向S-粗集被还原成Z.Pawlak粗集。利用单向S-粗集和单向S-粗集对偶给出具有属性析取特征的动态数据智能挖掘与应用;属性析取是数据具有的逻辑特征之一。主要结果是:利用单向S-粗集、单向S-粗集对偶结构,给出属性析取萎缩-扩张特征的动态数据生成与它的属性析取萎缩-扩张关系;给出数据推理与推理模型;利用数据推理给出动态数据智能挖掘定理;利用这些理论结果,给出动态数据智能挖掘-智能认知的应用。S-rough sets （singular rough sets） are generated by introducing dynamic characteristics into Z. Pawlak rought sets. S-rough sets have dynamic characteristics. In general, S-rough sets have three formulas including one direction S-rough sets, dual of one direction S-rough sets and two directions S-rough sets. Given certain conditions, one direction S-rough sets and dual of one direction S-rough sets can be reduced to Z. Pawlak rought sets. Dynamic data intelligent mining with attribute disjunctive characteristics was considered. Attribute disjunctive is one of logic characteristics of data. The main results of this paper include generation of dynamic data and the relations between dynamic data and its attribute disjunctive reduction and expansion, data reasoning and reasoning model, theorems of dynamic data intelligent mining,and applications of the obtained results.

关 键 词：S-粗集 属性析取 动态数据生成 数据推理 数据智能挖掘 应用 

分 类 号：O144[理学—数学] TP391[理学—基础数学]