粗糙集在知识约简中的应用  

Rough Set Knowledge Reduction Application

作　　者：吉胜军[1] JI Sheng-jun （Department of Soft Engineering Xi＇an University of Finance and Economics, Xi＇an 710100, China）

机构地区：[1]西安财经学院软件工程系,陕西西安710100

出　　处：《电脑知识与技术》2010年第7期5295-5296,共2页Computer Knowledge and Technology

摘　　要：将粗糙集应用于数据挖掘领域,能提高对不完整数据进行分析和学习的能力,属性约简能删除冗余属性,提高系统潜在知识的清晰度,降低发现规则的时间复杂性。目前已在人工智能、知识发现、模式分类等方面得到了应用。文中描述了粗糙集的基本理论,并通过实例分析了粗糙集理论在知识约简中的应用。Rough set data mining application, able to improve incomplete data, analysis and learning ability, attribute reduction can delete redundant attributes potential knowledge to improve the clarity of the system and reduce the time complexity of the rules found. Currently in artificial intelligence, knowledge discovery, pattern classification method can be applied. The article describes the basic theory of rough sets, and with an example of the rough set theory in knowledge reduction application.

关 键 词：粗糙集 不可区分关系 知识 约简 

分 类 号：TP301[自动化与计算机技术—计算机系统结构;自动化与计算机技术—计算机科学与技术]