A non-parameter outlier detection algorithm based on Natural Neighbor

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• 作者：Jinlong Huang ; Qingsheng Zhu ; ^{qszhu@cqu.edu.cn" class="auth_mail" title="E-mail the corresponding author} ; Lijun Yang ; Ji Feng
• 关键词：Outlier detection ; Natural Neighbor ; Natural Outlier Factor
• 刊名：Knowledge-Based Systems
• 出版年：2016
• 出版时间：15 January 2016
• 年：2016
• 卷：92
• 期：Complete
• 页码：71-77
• 全文大小：1798 K

文摘

Outlier detection is an important task in data mining with numerous applications, including credit card fraud detection, video surveillance, etc. Although many Outlier detection algorithm have been proposed. However, for most of these algorithms faced a serious problem that it is very difficult to select an appropriate parameter when they run on a dataset. In this paper we use the method of Natural Neighbor to adaptively obtain the parameter, named Natural Value. We also propose a novel notion that Natural Outlier Factor (NOF) to measure the outliers and provide the algorithm based on Natural Neighbor (NaN) that does not require any parameters to compute the NOF of the objects in the database. The formal analysis and experiments show that this method can achieve good performance in outlier detection.