A Reconfigurable Platform for Frequent Pattern Mining

In this paper, a new hardware architecture for frequent pattern mining based on a systolic tree structure is proposed. The goal of this architecture is to mimic the internal memory layout of the original FP-growth algorithm while achieving a much higher throughput. We also describe an embedded platform implementation of this architecture along with detailed analysis of area requirements and performance results for different configurations. Our results show that with an appropriate selection of tree size, the reconfigurable platform can be several orders of magnitude faster than the FP-growth algorithm.