电子学报2018，Vol.46Issue(2)：281-288,8.DOI:10.3969/j.issn.0372-2112.2018.02.004
双类型异质网中基于排序和聚类的离群点检测方法
An Outlier Detection Method Based on Ranking and Clustering in Bi-typed Heterogeneous Network
摘要
Abstract
Mining the outliers that are different from normal data objects in the network is one of the important tasks in data mining.At present,the research aiming at outlier detection in bi-typed heterogeneous information network is relatively small.The methods which are applicable to homogeneous network can not be applied to bi-typed heterogeneous networks. Therefore,we propose a Rank-Kmeans Based Outlier detection method,called RKBOutlier,in heterogeneous information net-work.The two kinds of the objects and the connected semantic information are extracted from the heterogeneous information network.One type of the objects is regarded as the attribute objects,another type of the objects is regarded as the target ob-jects.We perform cluster partitioning on target objects to detect the distribution of the attribute objects in each cluster.The objects which are abnormal at data distribution are considered to be the outliers.Ranking and clustering are combined to sig-nificantly improve the accuracy of clustering.The experimental results show that RKBOutlier can effectively detect outliers in bi-typed heterogeneous information network.关键词
离群点检测/排序/聚类/目标对象/属性对象Key words
outlier detection/ranking/clustering/target object/attribute object分类
信息技术与安全科学引用本文复制引用
彭涛,杨妮亚,徐原博,王冰冰,刘露..双类型异质网中基于排序和聚类的离群点检测方法[J].电子学报,2018,46(2):281-288,8.基金项目
国家自然科学基金(No.60903098) （No.60903098）
吉林大学研究生创新基金(No.2016183,No.2016184) （No.2016183,No.2016184）
