The FP-growth algorithm achieves better performance and efficiency than the Apriori-like algorithms because of avoiding costly candidate generation, but it still suffers from creating conditional FP-trees separately and recursively during the mining process, so its efficiency in time and space is not idear. In this paper, we propose a new algorithm CPM that designs a new tree structure called compressed prefix-tree, which stores all of the information in a highly compact form, decreases the consumption of system resources greatly. CPM mines frequent patterns in a depth-first order and directly in compressed prefix-trees by adjusting the node information and node links without using any additional data structures. Thus, it improves performance greatly.%针对FP-growth算法存在动态维护复杂、在挖掘过程中需要递归地创建大量的条件频繁模式树,导致时空效率不高等不足,本算法在压缩前缀树的基础上,通过调整树中节点信息和节点链,采用深度优先的策略挖掘频繁模式,无需任何附加的数据结构,极大地减少了系统资源的消耗,减少树的规模和遍历次数,挖掘效率大大提高.
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