An improved Outlier Detection Method in high-dimension Based on Weighted Hypergraph
Outlier detection in high-dimensional space is a hot topic in data mining, the main goal is to find out a small quantity of data objects with abnormal behavior in data set. In this paper, the concepts of the feature vector and the attribute similarity are defined, an improved algorithm SW HOT based on weighed hypergraph model for outlier detection in high dimensional space is presented. The objects in high dimensional space are translated into binary data type, by looking for the hyperedge of binary set, the data set hypergarph model is established, meanwhile, the weight of the hyperedge is equal to the value of the attribute similarity. In addition, the objects of the hypergraph are clustered by CURE algorithm, arbitrary shaped clusters can be identified. Furthermore, the outliers are found according to the point-to-window weighted support, the point-to-class belongingness and the point-to-window weighted deviation of size, the meaningful outliers in high-dimension can be mined by means of appropriate user-defined threshold. Experimental results show that SVVHOT can improve scaling and precision.
outlier detection clustering hypergraph weight similarity
YinZhao Li Di Wu JiaDong Ren ChangZhen Hu
Lab of Computer Network Defense Technology Beijing Institute of Technology Beijing 100081, China Department of Information and Electronic Engineering Hebei University of Engineering Handan 056038, College of Information Science and Engineering Yanshan University Qinhuangdao 066004, China School o
国际会议
Second International Symposium on Electronic Commerce and Security(第二届电子商务与安全国际研究大会)(ISECS 2009)
南昌
英文
815-819
2009-05-22（万方平台首次上网日期，不代表论文的发表时间）