摘要
现有离群数据研究主要集中于离群检测.为了对离群数据的来源、分类、含义、行为特征以及离群趋势等进行全面分析,以现有离群挖掘技术为基础,结合已提出的离群约简与关键域子空间等一系列概念及其搜索算法,定义了离群最近邻、原子离群类及离群变异类等概念,提出了离群簇分析及离群趋势分析方法,建立了一种完整的离群数据集特征描述及延伸知识发现的整体框架.通过对移动通信业务数据的离群分析进行具体讨论,表明了这种离群延伸知识发现框架在实际应用中的有效性.
The existing researches on outlier data mainly focus on the outlier detection. In order to completely analyze the origin, classification, meaning, behavior characteristics and outlying trend of outlier data, some concepts such as the nearest outlying neighbor, the atomic outlier class and the outlying mutation class are defined and the approaches to outlier clustering and outlying trend analyses are proposed based on the existing outlier mining techniques as well as a series of concepts and their searching algorithms including the outlying reduction and the key attribute subspace. Furthermore, an integrated framework of characteristic description and extended knowledge discovery of outlier data set is constructed, whose validity in practical applications is finally verified by the outlier analysis of mobile communication operation data.
出处
《华南理工大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2008年第9期31-36,共6页
Journal of South China University of Technology(Natural Science Edition)
基金
重庆市自然科学基金资助项目(2005BB2224)
教育部高校博士点基金资助项目(20050611027)
关键词
数据挖掘
离群分析
关键域子空间
知识发现框架
data mining
outlier analysis
key attribute subspace
knowledge discovery framework
