连续属性的离散化是数据预处理的重要工作。
Discretization of numeric attribute is an important role of data preprocessing.
连续属性的离散化是粗糙集理论的主要问题之一。
The discretization of real value attributes is one of the most main problems in rough sets theory.
连续属性的离散化在数据挖掘中有着非常重要的作用。
The discretization of continuous properties is very important in data mining.
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。
Discretization of continuous attributes is always one of the key problems that need urgent solutions in rough sets theory.
本文基于数值型关联规则的理论，用一种数理统计的方法进行连续属性的离散化。
Based on the theory of quantitative association rules, the numeric data is divided into intervals with statistical method.
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。
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.
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。
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.
数值型关联规则的算法大多是将多值属性关联规则挖掘问题转化为布尔型关联规则挖掘问题，而连续属性的离散化是数值型关联规则的核心问题。
Most quantitative association rules transform mining association rules of numeric property into boolean property, and the kernel problem is to divide the numeric data into intervals.
介绍了在数据库知识发现(KDD)中将连续属性离散化的一些方法，并提出使用值差分度量离散化的算法。
Some methods for dividing continuous attributes in KDD (knowledge discovery in database) and a method based on VDM (value difference metric) are presented.
改进了连续属性离散化的贪心算法。
Improve a greedy algorithm for discretization of continuous attribute.
基于多连续属性离散化的数据预处理方法。
A data preprocessing method based on multi continuous attribute discretization.
本文基于可辨识矩阵提出一种连续属性离散化的方法，并利用平均互信息量对离散化结果进行修正。
The paper puts forward a method of discretization of continuous properties based on discernibility matrix and revises the discrete result by average mutual information.
本文对基于粗集的数据预处理中数据补齐和连续属性离散化问题进行讨论。
This thesis discusses the question of data reinforce and continuous feature discretization which is based upon data preprocessing of rough set.
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。
Discretization of continuous type of attributes is a key issue in machine learning, it is a NP puzzle.
提出了一种基于断点重要性的配电网连续属性离散化方法，证明了该方法的有效性。
A continuous attribute discretization of the electric power distribution system is put forward based on the breakpoint importance, which is proved effectively.
应用聚类方法研究了数量关联规则提取过程中的连续属性离散化问题。
This paper presents a cluster method for discretization in the processing of mining quantitative association rules.
该方法打破了传统连续属性离散化遍历搜索的思路，在保证效率的基础上显著提高了离散效果。
This method break the idea of traditional continual attribute discretization's traversal heavy search, obviously enhanced the separate effect on the bases of efficiency.
针对这些问题，提出了一种基于属性重要度的整体连续属性离散化方法。
Regarding this, this paper puts forward the discrete method of the overall continuous attributes which is based on the importance of attributes.
通过对C4.5算法的研究与分析，针对该算法处理连续性属性的不足，采用一种基于信息熵的区间合并的属性离散化方法。
Based on C4.5 analysis and research, this paper gives the method of continuous attributes dispersed, that merge interval based on information entropy.
依据粗集理论研究离散化数据的特点，考虑类分布信息，采用信息熵理论进行连续条件属性的离散化。
Data discrimination is the character of RS, considering distributed information of class, and continual condition attributes are described according to information entropy theory.
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.
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。
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。
Most data mining and induction learning methods can only deal with discrete attributes; therefore, discretization of continuous attributes is necessary.
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