Alice and the Caterpillar: A more descriptive null model for assessing data mining results
Alice and the Caterpillar: A more descriptive null model for assessing data mining results
复制标题
DOI：
10.1109/icdm54844.2022.00052
复制
发表时间：
2022-11
影响因子：
2.7
通讯作者：
Giulia Preti;G. D. F. Morales;Matteo Riondato
中科院分区：
文献类型：
--
作者：
Giulia Preti;G. D. F. Morales;Matteo Riondato
我们介绍了使用统计假设检验来评估从观察到的二元交易和序列数据集获得的结果的新型无效模型。与现有数据集相比，我们的空模型维护观察到的数据集的属性更多。具体而言，它们保留了与数据集相对应的两分（多）图的二分联合度矩阵，该图可确保除其他模型所考虑的其他属性外，还保留了毛毛虫的数量，即长度三的路径。我们根据精心定义的状态集和在它们之间移动的有效操作，描述了Markov Chain Carlo算法的Markov Chain Carlo算法的Alice。我们的实验评估结果表明，爱丽丝会快速混合并缩放得很好，并且我们的无效模型发现与文献中先前考虑的结果不同。
We introduce novel null models for assessing the results obtained from observed binary transactional and sequence datasets, using statistical hypothesis testing. Our null models maintain more properties of the observed dataset than existing ones. Specifically, they preserve the Bipartite Joint Degree Matrix of the bipartite (multi-)graph corresponding to the dataset, which ensures that the number of caterpillars, i.e., paths of length three, is preserved, in addition to other properties considered by other models. We describe Alice , a suite of Markov chain Monte Carlo algorithms for sampling datasets from our null models, based on a carefully defined set of states and efficient operations to move between them. The results of our experimental evaluation show that Alice mixes fast and scales well, and that our null model finds different significant results than ones previously considered in the literature.