决策树算法及其在乳腺疾病图像数据挖掘中的应用

Decision Tree and Its Application in the Data Mining of Breast Disease Images

介绍了ID3决策树算法建立决策树的基本原理 ,着重介绍了决策树的修剪问题和两种典型的修剪算法———减少分类错误修剪算法和最小代价 复杂度修剪算法 ,并利用介绍的决策树算法和修剪算法对乳腺疾病图像进行数据挖掘 ,得到了一些有实际参考价值的规则 ,获得了很高的分类准确率 ,证明了决策树算法在医学图像数据挖掘领域有着广泛的应用前景。

Data mining is the nontrivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data. It can provide an useful path to acquire knowledge automatically. Decision tree classification algorithm is one of the most widely used algorithms in data mining. In this paper, ID3 decision tree constructing algorithm and two typical decision tree pruning algorithms are firstly analyzed. Then the introduced decision tree algorithms are applied to the data mining of the breast disease images and some valuable rules are obtained, greatly verifying the great potential of the decision tree algorithm to the data mining of medical images.