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：针对三维数据的三聚类方法通常需要指定各维度的聚类数量或簇大小。为解决这一问题，面向三阶张量的多片聚类（MSC）算法通过寻找位于低维子空间中的信号切片，基于阈值相似度对秩一张量数据集进行聚类。我们提出一种扩展算法MSC-DBSCAN，当数据集为r个秩一张量之和（r > 1）时，可从数据中提取位于不同子空间的不同切片簇。该算法采用与MSC相同的输入参数，且对于秩一张量数据能够获得与MSC相同的聚类结果。