Frequent similar pattern mining (FSP mining) allows for finding frequent patterns hidden from the classical approach. However, the use of similarity functions implies more computational effort, necessitating the development of more efficient algorithms for FSP mining. This work aims to improve the efficiency of mining all FSPs when using Boolean and non-increasing monotonic similarity functions. A data structure to condense an object description collection, named FV-Tree, and an algorithm for mining all FSPs from the FV-Tree, named X-FSPMiner, are proposed. The experimental results reveal that the novel algorithm X-FSPMiner vastly outperforms the state-of-the-art algorithms for mining all FSPs using Boolean and non-increasing monotonic similarity functions.

频繁相似模式挖掘（FSP 挖掘）可以找到经典方法中隐藏的频繁模式。然而，相似函数的使用意味着更多的计算工作，因此需要开发更有效的 FSP 挖掘算法。这项工作的目的是提高使用布尔和非递增单调相似函数时挖掘所有 FSP 的效率。提出了一种压缩对象描述集合的数据结构（名为FV-Tree ）和一种从FV-Tree中挖掘所有 FSP 的算法（名为X-FSPMiner ）。实验结果表明，新颖的算法X-FSPMiner远远优于使用布尔和非递增单调相似函数挖掘所有 FSP 的最先进算法。