Research Decision Tree and Design its System

决策树算法是一种重要的数据挖掘算法，主要用于预测数据的分类。决策树的基本算法有ID3和C4.5，其它算法一般只是基于这二个算法的扩展。CART和PUBLIC算法也是决策树的重要算法。决策树算法整个处理过程存在诸多局部的变化，如测试属性的度量标准、未知属性值的处理、连续值属性的离散化、决策树的修剪策略及数据集的划分和决策树的评估等。 本文提出了决策树算法“热点”问题。这些“热点”衍生于ID3和C4.5算法框架，主要包括算法的局部变化及算法的设计应当保证的良好扩展性。 设计模式是软件开发设计的经验，应用设计模式来处理决策树系统的“热点”问题，保证了一旦这些“热点”发生变化，系统可以最小的代价作...Decision tree is an important way of classification in Data Mining. Decision tree has some basic algorithms, such as ID3 and C4.5. CART and PUBLIC algorithms are two important algorithms. There are some local problems affecting the construction of decision tree, such as test attributes criterion selecting, missing value handling, continuous attributes discretizing, decision tree pruning, dataset d...学位：工学硕士院系专业：计算机与信息工程学院自动化系_系统工程学号：20013100

Decision Tree Constructing System Based on Design Patterns

基于决策树构造算法 ID3和C4.5,可衍生出诸多的算法变种.本文据此提出了决策树构造系统设计过程中的“热点”问题,对“热点”问题的不同处理方式即为算法的变种.同时应用设计模式来逐一解决这些问题,这样,保证了所得的决策树构造系统具有良好的可扩展性和可复用性,可适应多种算法的变种.Decision tree is an important way of classification in Data Mining.Decision tree has some basic algorithms,such as ID3 and C4.5.CART and PUBLIC algorithms are two important algorithms.Comparing these algorithms,we can know that there are some local changes affecting the construction of decision tree.This paper concludes a lot of“hot spots” in decision tree algorithm,such as data extracting,decision tree constructing,missing value handling,test attributes criterion selecting,decision tree pruning and decision tree describing,which include the local changes in algorithm fore mentioned and the design that should have good extensibility.Design patterns are experiences of software developments and designs.Applying design patterns to handle“hot spots”,the designs and codes of decision tree constructing system only revises little when these“hot spots”changed.Thus,this paper applies design patterns to design decision tree constructing system.Obviously,this solution has good extensibility and reusability