Multi-Slice Clustering for 3-order Tensor Data
Machine Learning
2021-09-23 v1 Machine Learning
Abstract
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.
Cite
@article{arxiv.2109.10803,
title = {Multi-Slice Clustering for 3-order Tensor Data},
author = {Dina Faneva Andriantsiory and Joseph Ben Geloun and Mustapha Lebbah},
journal= {arXiv preprint arXiv:2109.10803},
year = {2021}
}