# Parallel Computation of Multi-Slice Clustering of Third-Order Tensors

arXiv: https://arxiv.org/abs/2309.17383 · HTML: https://mlsanity.com/paper/2309.17383/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 23 references that are in the map.
- First posted on arXiv 29 Sep 2023.

## Builds on (most cited first, 6 of 6)

- [Manopt, a Matlab toolbox for optimization on manifolds](https://mlsanity.com/paper/1308.5200/) (2013)
- [Multiway clustering via tensor block models](https://mlsanity.com/paper/1906.03807/) (2019)
- [On Hyperparameter Search in Cluster Ensembles](https://mlsanity.com/paper/1803.11008/) (2018)
- [Multi-Slice Clustering for 3-order Tensor Data](https://mlsanity.com/paper/2109.10803/) (2021)
- [Multiway clustering of 3-order tensor via affinity matrix](https://mlsanity.com/paper/2303.07757/) (2023)
- [DBSCAN of Multi-Slice Clustering for Third-Order Tensors](https://mlsanity.com/paper/2303.07768/) (2023)

## Abstract (arXiv)

Machine Learning approaches like clustering methods deal with massive datasets that present an increasing challenge. We devise parallel algorithms to compute the Multi-Slice Clustering (MSC) for 3rd-order tensors. The MSC method is based on spectral analysis of the tensor slices and works independently on each tensor mode. Such features fit well in the parallel paradigm via a distributed memory system. We show that our parallel scheme outperforms sequential computing and allows for the scalability of the MSC method.