Detecting outliers on arbitrary data streams using anytime approaches

Data streams are gaining importance in many sensoring and monitoring environments. Frequent mining tasks on data streams include classification, modeling and outlier detection. Since often the data arrival rates vary, anytime algorithms have been proposed for stream clustering and classification, which can deliver a fast first result and improve their result if more time is available. In this work, we propose the novel concept of anytime outlier detection and introduce an algorithm for anytime outlier detection based on a hierarchical cluster representation. We show promising results in preliminary experiments and discuss future research for anytime outlier detection.