MINING MAIN POINTS OF KNOWLEDGE IN RELATIONAL DATABASES FOR CLASSIFICATION
Class association rules can be used not only for concept description but also for classification, so mining class association rules has drawn wide attention in data mining field.Specially, associative classification, which is based on class association rules, has been a hot spot in data mining research and machine learning community. At present, it is widely accepted that in general, associative classification has higheraccuracy and better robustness than decision treeclassification. However, associative classification has some deflects such as slow performance, huge memory usage and large-size classifying model. In this paper,a new idea is proposed to mine main points of knowledge in relational databases based on class association rules, which can be successfully used for fast,accurate and robust classification.
Data mining knowledge discovery main points of knowledge class association rules classification
XIAO-YUAN XU SHAO-DAN LIN GUO-QIANG HAN YU-REN ZHOU HUA-QING MIN
Faculty of Computer, Guangdong University of Technology, GZ 510090, Guangdong, China School of Computer Science and Engineering, South China University of Technology, GZ 510090, Guangdo
国际会议
2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)
大连
英文
1265-1270
2006-08-13（万方平台首次上网日期，不代表论文的发表时间）