摘要
讨论了利用领域知识,如完整性约束,分级概念等,对原数据库进行重新处理并且给那些丢失或明显背离常规的属性确定一个取值区间。实验证明利用这种基于属性的知识发现方法处理缺损数据是很有效的。
Incomplete data including noisy and missing data occurs frequently in data mining. In this paper we discuss the use of domain knowledge, such as integrity constraints or concept hierarchy, to reengineer the database and allocate sets to which missing or unacceptable outlying data may belong. Attributeoriented knowledge discovery method is proved to be a powerful approach for mining incomplete data in the large database in our experiment of this paper.
出处
《华东理工大学学报（自然科学版）》
CAS
CSCD
北大核心
2003年第6期646-648,652,共4页
Journal of East China University of Science and Technology
基金
上海市自然科学基金资助项目(01ZD4014)
关键词
缺损数据
数据挖掘
面向属性的知识发现
领域知识
incomplete data
data mining
attribute-oriented knowledge discovery
domain knowledge
