Intelligent knowledge discovery based on decision tree and extension theory

摘要：

　　Based on decision tree model and extension transformation theory, this paper designs an intelligent knowledge discovery algorithm.The algorithm applies secondorder mining on decision tree and association rules, so as to discover the principal property which can make the results totally different while the other conditions are the same except the values of itself.If the principal property can be transformed using extension transformation theory, well obtain dynamic knowledge from Rough Knowledge.In this way, we can solve contradictory issues and make reasonable and effective strategies which can convert from no to yes.Finally, the algorithm is verified on the data of an insurance company.

作者: L.L.Zhang J.Li R.Ren Y.B.Chen Y.Shi

作者单位: School of Management, Graduate University of Chinese Academy of Sciences, Beijing, China Research Centre on Fictitious Economy and Data Science, Chinese Academy of Sciences, Beijing, China

会议类型: 国际会议

会议名称: International Symposium on Extenics and Innovation Methods(可拓性与创新方法国际研讨会)

会议地点: 北京

会议语种:英文

页码: 143-148

在线出版日期: 2013-08-16（万方平台首次上网日期，不代表论文的发表时间）