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The problem of universal outlying sequence detection is studied, where thegoal is to detect outlying sequences among $M$ sequences of samples. A sequenceis considered as outlying if the observations therein are generated by adistribution different from those generating the observations in the majorityof the sequences. In the universal setting, we are interested in identifyingall the outlying sequences without knowing the underlying generatingdistributions. In this paper, a class of tests based on distribution clusteringis proposed. These tests are shown to be exponentially consistent with lineartime complexity in $M$. Numerical results demonstrate that our clustering-basedtests achieve similar performance to existing tests, while being considerablymore computationally efficient.

Linear-Complexity Exponentially-Consistent Tests for Universal Outlying Sequence Detection