摘要
决策树分类算法是数据挖掘研究中的一个以样本数据集为基础的归纳学习方法,它着眼于从一组无次序、无规则的样本数据集中推理出决策树表示形式的分类规则,提取描述样本数据集的数据模型。讨论了决策树分类算法的基本原理,给出了算法的特性并通过一个实例给出了具体的使用方法。
The decision tree classification algoriellm is a inductive learning method colich is based on the data sets in data-scooping research. The method foouseson deducing the classification rules in the form of decision tree from a group of random, irregular sample data sets, and drawing the data model which can descuibe the sample data sets. The basic principles of the method are also diseassed to point out its clcaracteristics,in which the real usage is illustrcted by an exanple.
出处
《盐城工学院学报（自然科学版）》
CAS
2005年第4期22-24,共3页
Journal of Yancheng Institute of Technology:Natural Science Edition
关键词
决策树
数据挖掘
信息增益
derision tree
data-scooping
information gain
