An algorithm for discretization based on Particle swarm optimization (PSO) is presented, which can settle the problem of continuous attributes discretization in systema modeling perfectly.
提出了一种基于微粒群优化(PSO)算法的连续属性离散化方法，很好的解决了建模过程中连续属性的离散化问题。
Discretization of continuous type of attributes is a key issue in machine learning, it is a NP puzzle.
连续型属性的离散化问题是机器学习中的关键问题，是一个NP难题。
Because traditional rough set theory can only deal with the discrete attributes in database. So, the discretization of continuous attributes is one of the main problems in rough sets.
由于粗糙集只能对离散属性进行处理，因而连续属性的离散化也就成了粗糙集的主要问题之一。
Discretization of continuous attributes is always one of the key problems that need urgent solutions in rough sets theory.
连续属性的离散化是粗糙集理论亟待解决的关键问题之一。
Based on theory of grey system and rough sets, a new discretization algorithm of continuous attributes in decision table is offered.
基于灰色系统和粗糙集的有关理论，提出了一种新的基于属性重要性的离散化算法。
Most data mining and induction learning methods can only deal with discrete attributes; therefore, discretization of continuous attributes is necessary.
很多数据挖掘方法只能处理离散值的属性，因此，连续属性必须进行离散化。
The discretization of Continuous attributes is one of the important contents in application study of rough sets.
连续属性离散化是粗糙集应用研究的重点内容之一。
The discretization of Continuous attributes is one of the important contents in application study of rough sets.
连续属性离散化是粗糙集应用研究的重点内容之一。
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