本文总结了有代表性的决策树算法。
提出了一种基于类别特征矩阵的决策树算法。
An algorithm of the decision tree based on class feature matrix is proposed.
基于分类决策树算法，建立了一个挖掘体系。
An intelligent mining system is created based on the decision tree algorithm.
目的研究决策树算法在医学图像数据挖掘中的应用。
Aim To study the application of decision tree algorithm for medical image data mining.
提出了一种避免了多值偏向问题的决策树算法——AF算法；
Second, this paper proposes a new decision tree algorithm, AF algorithm, which avoids multivalue bios.
提出了一种适合于大规模高维数据库的组合优化决策树算法。
A combined optimization decision tree algorithm suitable for a large scale and high dimension data-base is presented.
决策树算法通过构造精度高、小规模的决策树采掘训练集中的分类知识。
Decision tree algorithm is that the category knowledge of the training set is mined through built high precision and small-scale decision tree.
本文比较和分析了几种典型的决策树算法，着重对ID 3算法和C4。
This thesis compares and analyzes the typical decision tree algorithms, the ID3 algorithm and C4.
实验结果表明，应用GP决策树算法能够正确完成对趋势预测模型的选择。
Experimental results show that the choice for trend forecasting models can be correctly finished by using GP-decision tree algorithm.
该文提出了支持挖掘模型交换和移动通信客户流失分析的决策树算法框架。
This paper proposes a framework for decision tree construction algorithms that supports both model exchange and mobile communication churn analysis.
通过对决策树分类算法的比较，本文采用C4.5决策树算法实现自学习模块。
Comparing with Decision Tree algorithms, this system chooses the C4.5 to realize the self-learning module.
论文在探讨数据挖掘的一般理论的基础上，对数据挖掘中决策树算法进行了详细地介绍。
Main contents of the paper are summarized as following: 1 the essential principle of data mining was researched and several kinds of algorithm of data mining were given in this paper.
随后，着重分析了决策树算法的理论背景以及实现步骤，并给出了C4.5算法的伪码实现。
Then, the paper emphasizes the theory and method of Decision Tree algorithm and realizes C4. 5 algorithm.
文中详细阐述了几种极具代表性的决策树算法：包括使用信息熵原理分割样本集的ID3算法；
Firstly, some basic algorithms for inducing decision tree are discussed, including ID3, which uses information gain to select a splitting attribute when partitioning a training set;
因此，进一步改进决策树算法，使其更加适合数据挖掘的应用要求，具有重要的理论和现实意义。
Therefore, It possesses important theoretic and practical significance to make further improvement of decision tree algorithm, make it more suitable for data mining application requirements.
方法利用决策树算法对乳腺癌图像数据进行分类，提出了一个基于决策树算法的医学图像分类器。
Methods decision tree algorithms are applied to the data mining of the mammography classification, proposes a medical images classifier based on decision tree algorithm.
另一方面，本文还利用决策树算法，对影响学生就业的因素进行了分析，得到了一些有价值的模式。
And on other hand, the thesis also analyzes the factors that affect the jobs of graduated student by adopting decision tree algorithm, and some valuable models are achieved.
本文主要是研究数据挖掘中的决策树算法以及决策树算法在具体的小灵通流失分析中的研究与分析。
This essay is researching the Decision Tree Algorithm of Data Mining and the use in the Customer Drain analysis.
在传统的决策树算法中引入标量积协议，既保持决策树算法本身的优点，又满足了保持隐私的需求。
A decision tree classifier was applied and a scalar product protocol was added, so that the need of privacy preserving is satisfied as well as the advantage of decision tree is retained.
通常，模糊决策树算法是在清晰决策树算法的基础上进行改进得到的，是对清晰决策树算法的扩展。
Typically, the fuzzy decision tree algorithm is an improvement of the crisp decision tree algorithm, and is an extension of the crisp decision tree algorithm.
实验方法是分析决策树算法中的多值偏向问题的传统方法，其缺点是需要具备该数据领域的专家知识。
Experiment is the traditional method for analysing multivalue BIOS of decision tree algorithm, but it has a fault that we must have the expertise of the specific field.
多值偏向是决策树算法中普遍存在的问题，以往人们对于多值偏向问题的分析主要是基于实验观测的。
Variety bias exists in many decision tree algorithms, and in the past people analyzed this problem mainly based on experiments.
同时详细的阐述了决策树分类算法，并对比较流行的决策树算法id3、C4.5等算法进行详细分析与比较。
Meanwhile it describes the decision tree classification algorithm in detail, analyzes the ID3, C4.5 and other prevalent decision tree algorithm.
在语法层面，本文采用C4.5决策树算法将问题分类为是非问句、正反问句、特选问句和特指问句四种类型；
In syntax level, we classify the question into four categories by C4.5 algorithm, such as yes-or-no interrogative, specially related interrogative, and so on.
传统的ID3决策树算法以信息增益作为属性选择的准则值，但是信息增益大的属性并不一定就是有价值的属性。
Information gain is the measurement of the attributes selection in classical decision tree algorithm-ID3, but the attributes with high information gain is not always the valuable attributes.
现有的分类预测的方法有许多种，常见的有决策树算法(C4.5)、贝叶斯分类算法、BP算法与支持向量机等。
There are many classification methods to forecast such as decision tree algorithm (C4.5), Bayes algorithm, BP algorithm and SVM.
现有的分类预测的方法有许多种，常见的有决策树算法(C4.5)、贝叶斯分类算法、BP算法与支持向量机等。
There are many classification methods to forecast such as decision tree algorithm (C4.5), Bayes algorithm, BP algorithm and SVM.
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