Standard one-way clustering methods form homogeneous groups in a set of objects. Biclustering (or, two-way clustering) methods simultaneously cluster rows and columns of a rectangular data array in such a way that responses are homogeneous for all row-cluster by column-cluster cells. We propose a Bayes methodology for biclustering and corresponding MCMC algorithms. Our method not only identifies homogeneous biclusters, but also provides posterior probabilities that particular instances or features are clustered together. We further extend our proposal to address the biclustering problem under the commonly occurring situation of incomplete datasets. In addition to identifying homogeneous sets of rows and sets of columns, as in the complete data scenario, our approach also generates plausible predictions for missing/unobserved entries in the rectangular data array. Performances of our methodology are illustrated through simulation studies and applications to real datasets.

中文翻译：

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一些贝叶斯双聚类方法：建模和推理

标准的单向聚类方法在一组对象中形成同质组。双聚类（或双向聚类）方法同时对矩形数据数组的行和列进行聚类，使得响应对于所有行聚类按列聚类单元都是同质的。我们提出了一种用于双聚类和相应 MCMC 算法的贝叶斯方法。我们的方法不仅可以识别同质双聚类，还可以提供特定实例或特征聚集在一起的后验概率。我们进一步扩展了我们的建议，以解决在不完整数据集的普遍情况下的双聚类问题。除了在完整数据场景中识别同质的行集和列集之外，我们的方法还可以为矩形数据数组中缺失/未观察到的条目生成合理的预测。我们的方法的性能通过模拟研究和对真实数据集的应用来说明。