A Method of Detecting Outliers Matching User's intentions

Outlier detection has many applications like fraud detection, medical analysis, etc. Recently, several methods for finding outliers in large datasets have been reported. These existing techniques traditionally detect based on some prescribed definitions of outliers. However, it is very difficult for a user to decide the definition of outliers in prior. Usually, they have a few outlier examples in hand, and want to find more objects just like those examples. To solve this problem, we propose a novel method to detect outliers adaptive to users’ intensions implied by the outlier examples. This is, to the best of our knowledge, the first that detect outliers based on user-provided examples. Our experiments on both synthetic and real datasets show that the method has the ability to discover outliers that match the users’ intentions.