决策树算法分析报告

摘要

随着信息科技的高速发展，人们对于积累的海量数据量的处理工作也日益增重，需发明之母，数据挖掘技术就是为了顺应这种需求而发展起来的一种数据处理技术。

数据挖掘技术又称数据库中的知识发现，是从一个大规模的数据库的数据中有效地、隐含的、以前未知的、有潜在使用价值的信息的过程。决策树算法是数据挖掘中重要的分类方法，基于决策树的各种算法在执行速度、可扩展性、输出结果的可理解性、分类预测的准确性等方面各有千秋，在各个领域广泛应用且已经有了许多成熟的系统，如语音识别、模式识别和专家系统等。本文着重研究和比较了几种典型的决策树算法，并对决策树算法的应用进行举例。

关键词：数据挖掘；决策树；比较

Abstract

With the rapid development of Information Technology, people are f acing much more work load in dealing with the accumulated mass data.

Data mining technology is also called the knowledge discovery in database, data from a large database of effectively, implicit, previou sly unknown and potentially use value of information process. Algorithm of decision tree in data mining is an important method of classification based on decision tree algorithms, in execution speed, scalability, output result comprehensibility, classification accuracy, each has its own merits., extensive application in various fields and have many mature system, such as speech recognition, pattern recognition and expert system and so on. This paper studies and compares several kinds of typical decision tree algorithm, and the algorithm of decision tree application examples.

Keywords: Data mining; decision tree；Compare

目录

第一章绪论.................................... ４第二章文献综述................................ ４

2.1 数据挖掘简述........................................................ ４

2.2 决策树算法背景知识及研究现状........................................ ５

2.2.1 决策树算法描述................................................ ５

2.2.2关联分析决策树算法研究现状.................................... ６第三章决策树算法............................... ６

3.1 CLS算法............................................................ ６

3.2 ID3算法............................................................ ８

3.2.1 信息量大小的度量.............................................. ８

3.2.2 ID3决策树应用举例............................................ ９

3.3 C

4.5算法......................................................... １１

3.3.1 用信息增益率选择属性........................................ １２

3.3.2 处理连续属性值.............................................. １２

3.3 树剪枝............................................................ １３

3.4 weka平台的简述................................................... １３第四章决策树在学生成绩中的应用... 错误!未定义书签。

4.1数据的预处理......................................... 错误!未定义书签。

4.2数据的训练集处理..................................... 错误!未定义书签。

4.3数据的校验和成绩分析................................. 错误!未定义书签。第五章结论................................... １４参考文献...................................... I