DBSCAN of Multi-Slice Clustering for Third-Order Tensors

Dina Faneva Andriantsiory, Joseph Ben Geloun, Mustapha Lebbah
3/14/2023
cs.LGstat.ML

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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.

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Cite this paper

@article{andriantsiory2023dbscan,
  title  = {DBSCAN of Multi-Slice Clustering for Third-Order Tensors},
  author = {Dina Faneva Andriantsiory and Joseph Ben Geloun and Mustapha Lebbah},
  year   = {2023},
  eprint = {2303.07768},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2303.07768v3}
}

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