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：三组三维数据的若干方法要求具体指定的每个维度的组群大小或组群数量。为解决这一问题,用于三阶高的多切群集(MSC)发现信号片,它们位于一个一等一强数据集的低维子空间中,以便根据临界值相似性找到一个组群。我们建议采用一个称为MSC-DBSCAN的扩展算法,从数据中提取位于不同子空格的不同组群,如果数据集是 r 级一兆瓦之和(r > 1),那么我们的算法使用与MSC算法相同的输入,并且可以找到与MSC相同的第1 兆瓦数据。</s>