提出更精确的随机模型，评估数据挖掘结果的显著性。
Alice and the Caterpillar: A more descriptive null model for assessing data mining results
- 基于双分图度矩阵构建新零模型，保留路径结构特征。
- 实验显示模型能快速混合且识别出文献中未发现的显著模式。
- 适合需要严格验证数据挖掘结果的研究者使用。
我们为二值事务和序列数据集引入了新的零模型，用于通过统计假设检验评估数据挖掘结果。与现有模型相比，我们的零模型保留了更多观测数据集的特性，特别是保持了对应数据集的二分图（多重图）的双分联合度矩阵，从而确保三阶路径（即“毛毛虫”）的数量不变，同时保留其他已有模型考虑的属性。我们提出了Alice，一套基于马尔可夫链蒙特卡洛算法的采样工具，其状态空间定义严谨，状态转移操作高效。实验评估表明，Alice具有快速混合和良好可扩展性，并且所提出的零模型发现了以往研究中未被识别出的显著结果。
原文摘要 · Abstract (English)
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.
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