DBSCAN of Multi-Slice Clustering for Third-Order Tensors
Abstract
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.
Keywords
Cite
@article{arxiv.2303.07768,
title = {DBSCAN of Multi-Slice Clustering for Third-Order Tensors},
author = {Dina Faneva Andriantsiory and Joseph Ben Geloun and Mustapha Lebbah},
journal= {arXiv preprint arXiv:2303.07768},
year = {2023}
}
Comments
13 pages, improved version, typos removed, text restructured, same results