Several methods of triclustering of three dimensional data require the specification of the cluster size in each dimension. This introduces a certain degree of arbitrariness. To address this issue, we propose a new method, namely the multi-slice clustering (MSC) for a 3-order tensor data set. We analyse, in each dimension or tensor mode, the spectral decomposition of each tensor slice, i.e. a matrix. Thus, we define a similarity measure between matrix slices up to a threshold (precision) parameter, and from that, identify a cluster. The intersection of all partial clusters provides the desired triclustering. The effectiveness of our algorithm is shown on both synthetic and real-world data sets.

翻译：三维数据三组化的几种方法要求各维的组群尺寸的规格。 这引入了一定程度的任意性。 为了解决这个问题, 我们提议了一种新的方法, 即三级强力数据集的多虱群集( MSC) 。 我们在每个维度或振度模式中分析每个振动切片的光谱分解, 即矩阵。 因此, 我们定义了矩阵切片至临界值( 精度) 参数之间的类似度度, 并从中确定一个组群。 所有部分组群的交叉点提供了想要的三组群。 我们的算法的有效性表现在合成和真实世界的数据集上。