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.
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.
连续属性离散化方法在人工智能、机器学习等很多方面具有重要应用。
Discretization algorithm for real value attributes is of very important USES in many areas such as intelligence and machine learning.
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.
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.
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.
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.
首先提出主泛化决策等概念，在数据过滤方法的基础上，利用等价类的合并对属性离散化。
Its basic idea is to merge the equivalence classes of q on basis of the methods of data filtering.
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.
肿瘤诊断数据库中的属性常为数量型属性，因此如何将数量型属性离散化是挖掘关联规则的难点。
Attributes in the database of tumor diagnoses are usually quantitative attributes, so quantitative attribute discretization is a problem of mining association rules.
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.
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.
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.
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.
封装：软件对象就是包含模拟真实世界的对象的物理属性(数据)和功能(行为)的离散包。
Encapsulation: a software object is a discrete package containing both the physical properties (data) and the functionality (behavior) that mimics its real-world counterpart.
我们利用一份简短的结构性问卷对加纳所有四年级医科学生开展了离散选择试验，并使用组合logit模型来估计每个工作属性的效用。
We conducted the DCE among all fourth year medical students in Ghana using a brief structured questionnaire and used mixed logit models to estimate the utility of each job attribute.
随后论文重点对作者在数据离散和属性约简两个方面做的研究工作进行了阐述。
Then the author's researches on data discretization and attribute reduction are introduced in detail.
本文提出了一个新的决策表离散化算法，该算法在离散化数据的同时具有良好的属性约简功能。
In this paper, a novel decision table discretization algorithm is presented, which has fine attribute reduction function in time of data discretization and increases quality of classification.
连续属性通过极大熵方法离散化。
Continuous attributes are discretized through maximum entropy method.
本文针对传统的离散化技术所造成的信息丢失问题，提出了利用直觉模糊粗糙集合理论来进行属性约简的方法。
The technology of attribute reduction based on the intuitionistic fuzzy rough set theory is described as to the problem of information loss in the process of discretization.
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的主要问题之一。
The discretization of real value attributes is one of the most main problems in rough sets theory.
实际问题中经常涉及连续的数值属性，然而许多归纳学习算法却是针对离散属性空间的。
The continuous attribute problems are often encountered in the real world, but many outstanding inductive learning algorithms are mainly based on a discrete feature space.
前者从数据的离散化，降维，和属性选择方面有效的解决了处理大规模高维数据库时的效率与精度之间的矛盾。
The former algorithm makes improvements from three aspects: discretization, reducing dimension, attribute selection, which effectively solves the conflict between efficiency and prediction precision.
论文分析了基于熵的离散化方法的不足，从估计训练样本的概率分布的角度出发，提出基于样本分布与熵相结合的处理数值型属性的方法。
By the method of estimating the probability distribution of training examples, a new and simple method of dealing with numeric attribute based on example distribution and entropy is turned out.
机器学习中很多方法要求目标属性是离散的，而实际中很多属性是连续的。
The discrete attributes were required by voluminous methods on machine learning, but continuous attributes are universal in practice.
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.
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。
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。
Based on theory of grey system and rough sets, a new discretization algorithm of continuous attributes in decision table is offered.
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