摘要： 通过对多关系决策树学习算法MRDTL-2进行研究与分析，针对其运行效率较低和不能有效处理丢失属性值的问题，提出一种改进的多关系数据挖掘(IMRDTL)算法。在IMRDTL算法中，利用元组ID传播技术来进一步提高MRDTL-2算法的运行效率，同时使用广义朴素贝叶斯分类器来填补丢失的属性值，以进一步提高算法的准确率。
关键词:
多关系数据挖掘,
决策树,
元组ID传播,
广义朴素贝叶斯
Abstract: Through researches and analyses on Multi-Relational Decision Tree Learning(MRDTL) algorithm, MRDTL-2, for the problems of low running efficiency and poor effect in dealing with missing attribute values of it, this paper introduces an Improved Multi-Relational Data Mining (IMRDTL) algorithm. IMRDTL algorithm makes use of the tuple ID propagation for speeding up the efficiency of MRDTL-2 algorithm, meanwhile, generalized naive Bayes classifier is used to fill in missing attribute values, so the algorithm can provide a better accuracy.
Key words:
Multi-Relational Data Mining(MRDM),
decision tree,
tuple ID propagation,
generalized naive Bayes
中图分类号:
谢志强;于 旭;杨 静;刘若铎. 多关系决策树学习算法的研究与改进[J]. 计算机工程, 2009, 35(8): 50-52.
XIE Zhi-qiang; YU Xu; YANG Jing; LIU Ruo-duo. Research and Improvement of Multi-relational Decision Tree Learning Algorithm[J]. Computer Engineering, 2009, 35(8): 50-52.