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However, most existing methods are limited to a single context defined by user-specified features. In practice, identifying the right context is not trivial, even for domain experts. Moreover, for high-dimensional data, the notion of meaningful contexts that can unveil anomalies becomes substantially more complex. For instance, multiple useful contexts can often capture different phenomena. In this work, we introduce <jats:sc>Con<\/jats:sc>\n            <jats:sc>Quest<\/jats:sc>, a new unsupervised contextual anomaly detection approach that automatically discovers and incorporates multiple contexts useful for detecting and interpreting anomalies. Through experiments on 25 datasets, we show that <jats:sc>Con<\/jats:sc>\n            <jats:sc>Quest<\/jats:sc> outperforms various state-of-the-art methods. 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