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This paper addresses a major challenge in data mining applications where the full information about the underlying processes, such as sensor networks or large online database, cannot be practically obtained due to physical limitations such as low bandwidth or memory, storage, or computing power. Motivated by the recent theory on direct information sampling called compressed sensing (CS), we propose a framework for detecting anomalies from these largescale data mining applications where the fulldoi:10.1109/icdm.2009.110 dblp:conf/icdm/SahaPLV09 fatcat:gsa2k4d6uvejblm7gasswrbjdm