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We introduce anomaly clustering, whose goal is to group data into semantically coherent clusters of anomaly types. This is different from anomaly detection, whose goal is to divide anomalies from normal data. Unlike object-centered image clustering applications, anomaly clustering is particularly challenging as anomalous patterns are subtle and local. We present a simple yet effective clustering framework using a patch-based pretrained deep embeddings and off-the-shelf clustering methods. WearXiv:2112.11573v1 fatcat:swvhwaq6tjcppo77wg2qtmi5bq