Deriving frequent itemsets from databases is an important research issue in data mining. The number of frequent itemsets may be unusually large when a low min- imum support threshold is given. As such, the design of a compact representation to compress and describe them is an interesting topic. In the past, most related research on compact representation focused on frequent closed item- sets and frequent maximal itemsets. The former is a lossless
compact technology that can totally recover all frequent item-sets and their frequencies. Contrarily, the latter may lose some information regarding frequent itemsets, because it reserves frequent itemsets only and is unable to identify their frequency. In this paper, we propose a new compact
representation that lies between closed itemsets and maxi-mal itemsets. It can reserve all frequent itemsets and identify their approximate frequency. In addition, an efficient algo-rithm that corresponds to this new concept is designed to find related key information in databases. Finally, a series
of experiments are conducted to show the effectiveness of compact representation and the performance of the proposed algorithm.

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