The　density　peak　clustering　(DPC)　algorithm　identifies　patterns　in　high-dimensional　data　and　obtains　robust　outcomes　across　diverse　data　types　with　minimal　hyperparameters.　However,　DPC　may　produce　inaccurate　pattern　sizes　in　multi-dimensional　datasets　and　exhibit　poor　performance　in　recognizing　similar　patterns.　To　solve　these　issues,　we　propose　the　rediscover　and　subdivide　density　peak　clustering　algorithm　(RSDPC),　which　follows　three　key　strategies.　The　first　strategy,　rediscover,　iteratively　uncovers　prominent　patterns　within　the　existing　data.　The　second　strategy,　subdivide,　partitions　patterns　into　several　similar　subclasses.　The　third　strategy,　re-sort,　rectifies　errors　from　the　preceding　steps　by　incorporating　critical　distance　and　nearest　distance　considerations.　The　experimental　results　show　that　RSDPC　is　feasible　and　effective　in　synthetic　and　practical　datasets　compared　with　state-of-the-art　works.