An Outlier Mining Algorithm Based on Confidence Interval

摘要：

Outlier detection is a hot topic of data mining. After studying the existing classical algorithms of detecting outlier, this paper proposes an outlier mining algorithm based on confidence interval, and makes a new definition for outlier. The method combines mathematical statistics and densitybased clustering algorithm. It clustering firstly with DBSCAN algorithm, obtains credible sample and suspicious outliers. Secondly, a confidence interval is obtained based on credible sample, then suspicious outliers will be detected and disposed using the confidence interval. The experiment results on IRIS show that this algorithm can detect outliers effectively.

关键词： Outlier Clustering Confidence Interval Stratified sampling

作者: Yue Zhang Xuehua Yang Hang Li

作者单位: Software College Shenyang NormalUniversity Shenyang 110034, China Software College Shenyang Normal University Shenyang 110034, China

会议类型: 国际会议

会议名称: 2010 2nd IEEE International Conference on Information Management and Engineering(2010年IEEE第二届信息管理与工程国际会议 IEEE ICIME 2010)

会议地点: 成都

会议语种:英文

页码: 1-4

在线出版日期: 2010-04-16（万方平台首次上网日期，不代表论文的发表时间）