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Lecture Notes in Computer Science
Analysts often explore data cubes to identify anomalous regions that may represent problem areas or new opportunities. Discovery-driven exploration (proposed by S.Sarawagi et al. ) automatically detects and marks the exceptions for the user and reduces the reliance on manual discovery. However, when the data is large, it is hard to materialize the whole cube due to the limitation of both space and time. So, exploratory mining on complete cube cells needs to construct the data cubedoi:10.1007/3-540-47887-6_38 fatcat:5pefaw7dqjejvfxo5gyxucjx5e