# Faithful Density-Peaks Clustering via Matrix Computations on MPI Parallelization System

arXiv: https://arxiv.org/abs/2406.12297 · HTML: https://mlsanity.com/paper/2406.12297/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 31 references that are in the map.
- First posted on arXiv 18 Jun 2024.

## Builds on (most cited first, 5 of 5)

- [Contrastive Clustering](https://mlsanity.com/paper/2009.09687/) (2020)
- [Transductive Multi-view Zero-Shot Learning](https://mlsanity.com/paper/1501.04560/) (2015)
- [Clustering based Contrastive Learning for Improving Face Representations](https://mlsanity.com/paper/2004.02195/) (2020)
- [Fast Density-Peaks Clustering: Multicore-based Parallelization Approach](https://mlsanity.com/paper/2207.04649/) (2022)
- [Index-based Solutions for Efficient Density Peak Clustering](https://mlsanity.com/paper/2002.03182/) (2020)

## Abstract (arXiv)

Density peaks clustering (DP) has the ability of detecting clusters of arbitrary shape and clustering non-Euclidean space data, but its quadratic complexity in both computing and storage makes it difficult to scale for big data. Various approaches have been proposed in this regard, including MapReduce based distribution computing, multi-core parallelism, presentation transformation (e.g., kd-tree, Z-value), granular computing, and so forth. However, most of these existing methods face two limitations. One is their target datasets are mostly constrained to be in Euclidian space, the other is they emphasize only on local neighbors while ignoring global data distribution due to restriction to cut-off kernel when computing density. To address the two issues, we present a faithful and parallel DP method that makes use of two types of vector-like distance matrices and an inverse leading-node-finding policy. The method is implemented on a message passing interface (MPI) system. Extensive experiments showed that our method is capable of clustering non-Euclidean data such as in community detection, while outperforming the state-of-the-art counterpart methods in accuracy when clustering large Euclidean data. Our code is publicly available at https://github.com/alanxuji/FaithPDP.