摘要
决策树是数据挖掘分类问题算法中一种性能较好的算法,本文主要研究自决策树在数据挖掘中应用以来存在问题,主要是可扩展性问题。综述了国内外针对此问题所提出的解决方法,以及分析了改进算法的优缺点,以便有利于对决策树关键问题,即扩展性问题的研究。同时本论文中所研究的算法的思想也有助于数据挖掘中其它领域解决大数据集问题。
Among classification models for data exploring, Decision tree is a better model in performance. This article mainly deals with problem that produced since decision tree applied in data exploring. The main problem is scalability. This article overviewed the solution for this problem at home and abroad, and analyzed on the advantages and disadvantages of improved algorithms. That benefits for research on scalable decision tree. At the same time, the idea of algorithms researched in this article is helpful for solving huge data set problem in data exploration of other field.
出处
《惠州学院学报》
2009年第3期58-61,共4页
Journal of Huizhou University
关键词
决策树
分类挖掘
可扩展性
decision tree
classification mining
scalability
