LEARNING RULES FROM LARGE DATASETS USING ROUGH SET AND APRIORI ALGORITHM

摘要：

This paper presents a mechanism called R_Apriori for learning rules from large datasets. The existing rough set based methods are not applicable for large data sets for its high time and space complexity. In this paper, large data sets are divided into several parts, in combination with Apriori algorithm, implicated rules are derived in liner relation to size of data set. At last, experiment result proves that this method is prior to existing ones.

关键词： Rough set apriori rule derivation large dataset

作者: SEN GUO ZHI-YAN WANG YANG-QING ZHANG HE-PING YAN

作者单位: School of computer science and engineering, South China University of Technology, GuangZhou, 510640, China

会议类型: 国际会议

会议名称: 2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

会议地点: 大连

会议语种:英文

页码: 1178-1183

在线出版日期: 2006-08-13（万方平台首次上网日期，不代表论文的发表时间）