Outlier Detection in High Dimension Based on Projection

Outlier detection is one of the branches of data mining, with important applications in the domains of finance fraud detection, network intrusion analysis and so on. But most applications are high dimensional domains. Many algorithms use the concept of proximity to find outliers based on the relationship to the data set. However, the sparsity of high dimensional points results to the algorithms are not available for high dimensional space. In this paper, we discuss a new technique ODHDP (outlier detection in high dimension based on projection) which finds the outliers based on projection from the data set