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We propose a block-based sampling method (I-sampling) which randomly selects the base data blocks from the block pool of a large-scale dataset rather than directly chooses records from the original dataset. I-sampling firstly partitions the given large-scale dataset into the non-overlapping primary data blocks. Secondly, the records in each primary data block are randomly shuffled and the corresponding...
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