| 用粗糙集理论建立决策树的一种方法 |
| |
| 引用本文： | 刘旭敏,黄厚宽,徐维祥.用粗糙集理论建立决策树的一种方法[J].通讯和计算机,2005,2(8):37-40. |
| |
| 作者姓名： | 刘旭敏 黄厚宽 徐维祥 |
| |
| 作者单位： | [1]首都师范大学信息工程学院,北京100037 [2]北京交通大学计算机与信息技术学院,北京100044 [3]北京交通大学交通运输学院,北京100044 |
| |
| 摘 要： | 在数据挖掘中，分期是一个很重要的问题，有很多流行的分类器可以创建决策树木产生类模型。本文介绍了通过信息增益或熵的比较来构造一棵决策树的数桩挖掘算法思想，给出了用粗糙集理论构造决策树的一种方法，并用曲面造型方面的实例说明了决策树的生成过程。通过与ID3方法的比较，该种方法可以降低决策树的复杂性，优化决策树的结构，能挖掘较好的规则信息。
|
| 关 键 词： | 数据挖掘 粗糙集 决策树 信息熵 |
An Algorithm of Decision Tree Based on Rough Set Theory |
| |
| Xumin Liu , Houkuan Huang , Weixiang Xu.An Algorithm of Decision Tree Based on Rough Set Theory[J].Journal of Communication and Computer,2005,2(8):37-40. |
| |
| Authors: | Xumin Liu Houkuan Huang Weixiang Xu |
| |
| Abstract: | Classification is an important problem in data mining. A number of popular classifiers construct decision trees to generate class models. In this paper, the idea of algorithm for building a decision tree is introduced by comparing the algorithm of information gain or entropy. According to the theory of rough sets, the method of constructing decision tree is discussed. The produced process of decision tree is given as an example of surface modeling. Compared with ID3 algorithm, the complexity of decision tree is decreased, the construction of decision tree is optimized the better rule of data mining can be built. |
| |
| Keywords: | Data Mining Rough Sets Decision Tree Information Entropy |
| 本文献已被 维普 等数据库收录！ |
|
