基于决策树数据挖掘的分析与应用

决策树技术是一种对海量数据集进行分类的非常有效方法。通过构造决策树模型,提取有价值的分类规则,帮助决策者做出准确的预测已经应用在很多领域。基于这种技术构造的蘑菇可食用性决策树模型,提供了通过蘑菇属性判别蘑菇可食用性的科学依据。决策树算法采用C4.5算法,它把信息增益率作为属性选择的度量标准。从实验结果来看,决策树模型虽然显示了一个很不平衡的结构,但得出了很容易理解的决策规则。

Analysis and Application of Data Mining Algorithm Based on Decision Tree

The decision tree technique is a very effective method for classifying large data sets. By means of constructing a decision tree model, the technique picks up valuable classified rules, and helps the decision- makers to make out an exact forecast. The technique has widely applied in a great many fields. The technology is adopted to construct the decision tree model of the edibility of mushroom, which provides scientific basis for differentiating the edibility of mushroom by way of the mushroom property. The calculation of the decision tree uses the C4. 5 algorithm, which takes information gain ratio as attribute choice criterion. The experiment result shows that although the decision tree gives an unbalanced structure, understandable decision rules are obtained from the decision tree.