document: Detecting outliers using transduction and statistical testing Outlier detection can uncover malicious behavior in fields like intrusion detection and fraud analysis . Although there has been a significant amount of work in outlier detection , most of the algorithms proposed in the literature are based on a particular definition of outliers ( e.g. , density-based ) , and use ad-hoc thresholds to detect them . In this paper we present a novel technique to detect outliers with respect to an existing clustering model . However , the test can also be successfully utilized to recognize outliers when the clustering information is not available . Our method is based on Transductive Confidence Machines , which have been previously proposed as a mechanism to provide individual confidence measures on classification decisions . The test uses hypothesis testing to prove or disprove whether a point is fit to be in each of the clusters of the model . We experimentally demonstrate that the test is highly robust , and produces very few misdiagnosed points , even when no clustering information is available . Furthermore , our experiments demonstrate the robustness of our method under the circumstances of data contaminated by outliers . We finally show that our technique can be successfully applied to identify outliers in a noisy data set for which no information is available ( e.g. , ground truth , clustering structure , etc. ) . As such our proposed methodology is capable of bootstrapping from a noisy data set a clean one that can be used to identify future outliers .
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