DBSCAN of Multi-Slice Clustering for Third-Order Tensors
This work addresses a limitation in tensor clustering methods for researchers in data analysis, though it appears incremental as it builds directly on an existing algorithm.
The paper tackles the problem of triclustering three-dimensional data without requiring pre-specified cluster sizes or numbers, by extending Multi-Slice Clustering (MSC) with DBSCAN to handle datasets that are sums of multiple rank-one tensors, achieving the same solution as MSC for rank-one tensor data.
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.