Multiway clustering of 3-order tensor via affinity matrix
This addresses tensor data analysis for researchers in machine learning and data mining, but appears incremental as it builds on existing clustering techniques.
The paper tackles multiway clustering for 3-order tensors by proposing MCAM, a method that constructs an affinity matrix from tensor slices and applies clustering algorithms, achieving competitive results on synthetic and real datasets.
We propose a new method of multiway clustering for 3-order tensors via affinity matrix (MCAM). Based on a notion of similarity between the tensor slices and the spread of information of each slice, our model builds an affinity/similarity matrix on which we apply advanced clustering methods. The combination of all clusters of the three modes delivers the desired multiway clustering. Finally, MCAM achieves competitive results compared with other known algorithms on synthetics and real datasets.