In this paper, we propose self-semi-supervised clustering, a new clustering method for large scale data with a massive null group. Self-semi-supervised clustering is a two-stage procedure: preselect a part of “null” group from the data in the first stage and apply semi-supervised clustering to the rest of the data in the second stage, allowing them to be assigned to the null group. We evaluate the performance of the proposed method using a simulation study and demonstrate the method in the analysis of time course gene expression data from a longitudinal study of Influenza A virus infection.

中文翻译：

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具有大量空组的大规模数据的自我半监督聚类

在本文中，我们提出了自半监督聚类，这是一种针对具有大量空组的大规模数据的新聚类方法。自半监督聚类是一个分为两个阶段的过程：在第一阶段从数据中预选择“空”组的一部分，然后在第二阶段对其余数据应用半监督聚类，从而可以对其进行分配到空组。我们使用模拟研究评估了所提出方法的性能，并通过对A型流感病毒感染的纵向研究证明了该方法可用于分析时程基因表达数据。