文献类型：期刊文献

英文题名：Lazy decision tree method for distributed privacy preserving data mining

作者：Zhang, Huawei[1]

第一作者：张华伟

通讯作者：Zhang, H.|[1048412518b767f]张华;

机构：[1] College of Computer and Information Engineering, Henan University of Economics and Law, Zhengzhou 450003, China

第一机构：河南财经政法大学计算机与信息工程学院

通讯机构：[1]College of Computer and Information Engineering, Henan University of Economics and Law, Zhengzhou 450003, China|[1048412]河南财经政法大学计算机与信息工程学院;[10484]河南财经政法大学;

年份：2012

卷号：4

期号：14

起止页码：458-465

外文期刊名：International Journal of Advancements in Computing Technology

收录：EI(收录号：20123715422379);Scopus(收录号：2-s2.0-84865759721)

语种：英文

外文关键词：Data privacy - Decision trees - Trees (mathematics)

摘要：Privacy preserving data mining is an important research area right now, and the key issue is to develop data mining method under the premise of protecting privacy data. As an important method for classified data mining, decision tree is also one exceptional classified method that has been studied intensively in the area of privacy preserving data mining. This paper comprehensively summarized decision tree mining methods of privacy preserving, and suggested a highly efficient decision tree classified mining method for privacy preserving based on lazy decision tree. Experiment results proved this method considered the advantages of eager learning and lazy learning, and hence possessed higher flexibility and compatibility.