  Frequent-pattern mining is a common approach to reveal the valuable hidden
trends behind data. However, existing frequent-pattern mining algorithms are
designed for DRAM, instead of persistent memories (PMs), which can lead to
severe performance and energy overhead due to the utterly different
characteristics between DRAM and PMs when they are running on PMs. In this
paper, we propose an efficient and Wear-leveling-aware Frequent-Pattern Mining
scheme, WFPM, to solve this problem. The proposed WFPM is evaluated by a series
of experiments based on realistic datasets from diversified application
scenarios, where WFPM achieves 32.0% performance improvement and prolongs the
NVM lifetime of header table by 7.4x over the EvFP-Tree.
