MCRapper：偏序集族的Monte-Carlo Rademacher算法及近似模式挖掘
MCRapper: Monte-Carlo Rademacher Averages for Poset Families and Approximate Pattern Mining
摘要：
我们提出了MCRapper，一种有效计算具有偏序集的函数族的Monte-Carlo经验Rademacher Approach（MCERA）的算法（例如，格）结构，例如在许多模式挖掘任务中出现那些结构。MCERA允许我们计算样本均值与其期望值的最大偏差的上限，因此它可以用于找到两个显著函数（即，模式），以及高期望函数集合的近似（例如，频繁模式），当可用数据是来自大数据集的小样本时。此功能是对以前提出的只能实现两者之一的解决方案的强大改进。MCRapper使用函数差异的上限来有效地探索和修剪搜索空间，这是一种从模式挖掘本身借鉴的技术。为了展示MCRapper的实际用途，我们使用它来开发一个算法TFP-R的任务的真实频繁模式（TFP）挖掘。TFP-R保证了包含任何误报（精确度）的概率，并表现出比现有方法更高的统计能力（召回率）。我们评估MCRapper和TFP-R，并表明它们在各自的任务中表现优于最先进的技术。
英文摘要：
We present MCRapper, an algorithm for efficient computation of Monte-Carlo Empirical Rademacher Averages (MCERA) for families of functions exhibiting poset (e.g., lattice) structure, such as those that arise in many pattern mining tasks. The MCERA allows us to compute upper bounds to the maximum deviation of sample means from their expectations, thus it can be used to find both statistically-significant functions (i.e., patterns) when the available data is seen as a sample from an unknown distribution, and approximations of collections of high-expectation functions (e.g., frequent patterns) when the available data is a small sample from a large dataset. This feature is a strong improvement over previously proposed solutions that could only achieve one of the two. MCRapper uses upper bounds to the discrepancy of the functions to efficiently explore and prune the search space, a technique borrowed from pattern mining itself. To show the practical use of MCRapper, we employ it to develop an algorithm TFP-R for the task of True Frequent Pattern (TFP) mining. TFP-R gives guarantees on the probability of including any false positives (precision) and exhibits higher statistical power (recall) than existing methods offering the same guarantees. We evaluate MCRapper and TFP-R and show that they outperform the state-of-the-art for their respective tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
