基于粗糙集理论的属性离散化算法

决策系统中连续属性离散化，即将一个连续属性分为若干属性区间并为每个区间确定一个离散型数值，对后继阶段的机器学习具有重要的意义。首先研究了满足决策系统最优划分的一种计算候选断点集合的算法，然后在基于条件属性重要度和贪心算法的基础上提出了一种确定结果断点子集的新启发式算法。所提出的属性离散算法考虑并体现了粗糙集理论的基本特点和优点，并能取得较理想的连续属性离散化结果。

Method of data discretization based on rough set theory

The discretization of continuous attributes values of a decision system which divides continuous values into different space and allocates some discrete values to each space is always with great contribution to the machine learning.This paper studies a new algorithm of computing candidate cuts for best partition in decision system at first,and proposes one heuristic method based on the importance of condition attributes and greedy algorithm.The two algorithms consider specialty of rough set and embody the advantages of this theory.Moreover,excellent discretization results may be expected from them.