document: Outlier detection by sampling with accuracy guarantees An effective approach to detecting anomalous points in a data set is distance-based outlier detection . This paper describes a simple sampling algorithm to effciently detect distance-based outliers in domains where each and every distance computation is very expensive . Unlike any existing algorithms , the sampling algorithm requires a xed number of distance computations and can return good results with accuracy guarantees . The most computationally expensive aspect of estimating the accuracy of the result is sorting all of the distances computed by the sampling algorithm . The experimental study on two expensive domains as well as ten additional real-life datasets demonstrates both the effciency and effectiveness of the sampling algorithm in comparison with the state-of-the-art algorithm and there liability of the accuracy guarantees .
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