Rough Set理论中连续属性的离散化方法

A NEW METHOD OF DISCRETIZATION OF CONTINUOUS ATTRIBUTES IN ROUGH SETS

Rough Set(RS)理论是一种新的处理不精确、不完全与不相容知识的数学工具 .传统的 RS理论只能对数据库中的离散属性进行处理 ,而绝大多数现实的数据库既包含了离散属性 ,又包含了连续属性 .文中针对传统 RS理论的这一缺陷 ,利用决策表相容性的反馈信息 ,提出了一种领域独立的基于动态层次聚类的连续属性离散化算法 .该方法为 RS理论处理离散与连续属性提供了一种统一的框架 ,从而极大地拓广了 RS理论的应用范围 .通过一些例子将本算法与现有方法进行了比较分析 。

Rough set theory is a new mathematical tool to deal with imprecise, incomplete and inconsistent data. The traditional rough set theory can only deal with the discrete attributes in database. However, most real life databases consist of not only discrete attributes but also continuous attributes. In order to overcome the limitation of the traditional rough sets, using feedback information from decision table consistency we propose a new method of discretization of continuous attributes based on dynamic layer cluster. A unified framework of the rough set theory to deal with discrete and continuous attributes is suggested, which extends the scope of application of rough sets. The results of comparison between this method and some existing algorithms of discretization are encouraging.