Support Vector Clustering for Outlier Detection

摘要：

　　In this paper a novel Support vector clustering(SVC) method for outlier detection is proposed.Outlier detection algorithms have application in several tasks such as data mining,data preprocessing,data filter-cleaner,time series analysis and so on.Traditionally outlier detection methods are mostly based on modeling data based on its statistical properties and these approaches are only preferred when large scale set is available.To solve this problem,in this paper we focus on establishing the context of support vector clustering approach for outlier detection.Compared to traditional outlier detection methods,the performance of the SVC is not sensitive to the selection of needed parameters.The experiment results proved the efficiency of our method.

关键词： Support vector clustering Outlier detection Nearest Distance

作者: Hai-Lei Wang Wen-Bo Li Bing-Yu Sun

作者单位: the Institute of Intelligence Machines,Chinese Academy of Sciences ;the department of Automation, th the Institute of Intelligence Machines, Chinese Academy of Sciences Hefei, Anhui, China

会议类型: 国际会议

会议名称: 2012 2nd International Conference on Computer and Information Applications(ICCIA2012)(2012第二届计算机和信息应用国际会议)

会议地点: 太原

会议语种:英文

页码: 343-345

在线出版日期: 2012-12-08（万方平台首次上网日期，不代表论文的发表时间）