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Content addressable memory (CAM) plays an important role in the performance of many applications such as DCT transforms, processor caches, database accelerators, and network routers because it enables high-speed search operations with hardware acceleration. However, the power consumption of CAM is rather high because within CAM, searching is conducted in parallel for all registered words. Hence, pre-computation-based CAM, i.e., PB-CAM, was proposed in  in order to reduce the number of parallel-operated words by first filtering using a precomputation circuit called the parameter extractor. In this work, we propose a data driven algorithm - local grouping (LG) - to synthesize a parameter extractor for PB-CAM such that the registered data can be uniformly mapped to construct parameters; the cost of implementing the parameter extractor is also decreased. Moreover, we also adopt a discard and interlace (DAI) method that can further reduce the impact on non-uniform cases, which happens when most data are identical in some data blocks before LG processing. In experiments, average power consumption reduction of 60.4% was achieved and the number of CMOSs used was also reduced by 0.52%, when compared with the conventional gate-block selection algorithm .