Abstract: Association rule mining which is used to find the correlation of items is an important research direction in data mining.FP-growth algorithm greatly reduces the search time without generating candidate itemsets by constructing FP-tree to find frequent itemsets.The drawback of it is taking a lot of memory space.Based on the thinking of FP-growth algorithm，an algorithm for mining frequent patterns based on FS-tree（1 item frequent sub-tree） is proposed.The algorithm reduces the space complexity significantly by splitting FP-tree into some FS-trees.The experiments show that the algorithm is effective.

摘要： 关联规则挖掘是数据挖掘中的一个重要研究方向，用于发现项集之间的关联性。FP-growth算法通过构造FP-tree产生频繁集，由于其不生成候选集从而大大降低了搜索开销，其缺点是占用大量的内存空间。基于FP-growth的算法思想，提出基于FS-tree（频繁1-项子树）的频繁模式挖掘算法，通过将FP-tree拆分为多棵FS-tree，使算法的空间复杂度明显减小。实验表明，该算法是有效的。