决策树分类ID3算法研究

分类是数据挖掘的重要内容之一，在许多领域得到广泛应用，现已有多种分类方法，其中决策树分类法在海量数据环境中应用最为广泛，本文分析了决策树分类ID3算法的原理，给出构造决策树的基本算法，指出ID3算法构造决策树的优缺点，针对ID3算法倾向于取值较多的测试属性的缺点，引入一个参数来约束属性选择，给出一种优化算法．

Decision Tree Classification

Classification is an important problem in data mining. Classification now has been successfully applied to wide range of application areas. Many different techniques have been proposed for classification, decision tree classifiers have found the widest applicability in large-scale data mining environments. This thesis analyzes decision trees ID3 method of principle, given the basic method to build a decision tree, point out advantages and shortages when building a tree, due to the shortcomings that it inclined to more value test attribute of ID3 algorithm, the introduction of a parameter to restrain attribute choice,given an optimization algorithm.