Detecting outliers on arbitrary data streams using anytime approaches
Ira Assent, Philipp Kranen, Corinna Baldauf, Thomas Seidl · 2010
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.