以相关性确定条件属性的概化决策树  

A Generalized Decision Tree Using Relevance Analysis to Evaluate Condition Attributes

作　　者：刘健[1] 陈俊杰[1] 

机构地区：[1]太原理工大学计算机与软件学院,山西太原030024

出　　处：《太原理工大学学报》2006年第S1期26-29,共4页Journal of Taiyuan University of Technology

基　　金：教育部科学技术研究重点项目(03020);山西省自然科学基金资助项目(20031038)

摘　　要：在介绍了一些典型决策树分类算法的基础上,研究了一种基于相关性分析的决策树分类器。其主要思想是通过属性相关性来压缩训练集的大小并在建立决策树过程中采用此度量值来确定划分条件属性的顺序,通过阈值设定和处理简化了决策树的剪枝和优化过程,提高了处理的效率和规模。文章详细描述了算法的执行过程以及正确性证明和时间复杂性分析。Efficiency and scalability are fundamental issues concerning data mining in large databases.The decision tree is an important classifier in data mining.In this paper a decision tree classifier based on relevance analysis is proposed after discussing traditional algorithms.The main idea is to compact the training data and evaluate condition attributes with correlations,and made pruning and optimization process simplified in order to get high accuracy and fast classifying speed,which leads to efficient,high-q...

关 键 词：决策树 分类 相关性分析 效率 规模 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]