An innovative FTL approach (ASA-FTL) for identifying and separating hot/cold data at the page level based on data clustering for SSDs; A sampling and selective caching technique to control the overhead of clustering-based hot/cold data separation approach; A Hotness Updating Algorithm that samples and clusters data periodically to update the separation criteria and an overhead analysis of the approach; An evaluation of the proposed approach using the FlashSim simulator with several real world workloads, showing the performance benefits of our approach compared to a current state of the art FTL; An evaluation of the effect of the sample size and the adaptivity of the proposed ASA-FTL.