Several methods for triclustering three-dimensional data require the cluster size or the number of clusters in each dimension to be specified. To address this issue, the Multi-Slice Clustering (MSC) for 3-order tensor finds signal slices that lie in a low dimensional subspace for a rank-one tensor dataset in order to find a cluster based on the threshold similarity. We propose an extension algorithm called MSC-DBSCAN to extract the different clusters of slices that lie in the different subspaces from the data if the dataset is a sum of r rank-one tensor (r > 1). Our algorithm uses the same input as the MSC algorithm and can find the same solution for rank-one tensor data as MSC.

翻译：在三维数据的三元聚类中，有几种方法需要指定聚类大小或每个维度中的聚类数量。为了解决这个问题，针对秩为一的张量数据集，Multi-Slice Clustering (MSC) for 3-order tensor找到位于低维子空间中的信号切片以基于阈值相似性找到聚类。我们提出了一种扩展算法MSC-DBSCAN，以从数据中提取位于不同子空间中的不同切片聚类，如果数据集是r（r > 1）个秩为一的张量的总和，则使用相同输入作为MSC算法，可以在秩为一的张量数据上找到相同的解决方案。