# DBSCAN of Multi-Slice Clustering for Third-Order Tensors

arXiv: https://arxiv.org/abs/2303.07768 · HTML: https://mlsanity.com/paper/2303.07768/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 20 references that are in the map.
- First posted on arXiv 14 Mar 2023.

## Builds on (most cited first, 4 of 4)

- [Computational and Statistical Boundaries for Submatrix Localization in a Large Noisy Matrix](https://mlsanity.com/paper/1502.01988/) (2015)
- [Dynamic Tensor Clustering](https://mlsanity.com/paper/1708.07259/) (2017)
- [Multi-Slice Clustering for 3-order Tensor Data](https://mlsanity.com/paper/2109.10803/) (2021)
- [Algorithms for an Efficient Tensor Biclustering](https://mlsanity.com/paper/1903.04042/) (2019)

## Built on by (most cited first, 1 of 1)

- [Parallel Computation of Multi-Slice Clustering of Third-Order Tensors](https://mlsanity.com/paper/2309.17383/) (2023)

## Abstract (arXiv)

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.