# An Improved Probability Propagation Algorithm for Density Peak Clustering Based on Natural Nearest Neighborhood

arXiv: https://arxiv.org/abs/2207.01178 · HTML: https://mlsanity.com/paper/2207.01178/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 23 references that are in the map.
- First posted on arXiv 4 Jul 2022.

## Builds on (most cited first, 1 of 1)

- [RECOME: a New Density-Based Clustering Algorithm Using Relative KNN Kernel Density](https://mlsanity.com/paper/1611.00484/) (2016)

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

- [SDC-HSDD-NDSA: Structure Detecting Cluster by Hierarchical Secondary Directed Differential with Normalized Density and Self-Adaption](https://mlsanity.com/paper/2307.00677/) (2023)

## Abstract (arXiv)

Clustering by fast search and find of density peaks (DPC) (Since, 2014) has been proven to be a promising clustering approach that efficiently discovers the centers of clusters by finding the density peaks. The accuracy of DPC depends on the cutoff distance (dc), the cluster number (k) and the selection of the centers of clusters. Moreover, the final allocation strategy is sensitive and has poor fault tolerance. The shortcomings above make the algorithm sensitive to parameters and only applicable for some specific datasets. To overcome the limitations of DPC, this paper presents an improved probability propagation algorithm for density peak clustering based on the natural nearest neighborhood (DPC-PPNNN). By introducing the idea of natural nearest neighborhood and probability propagation, DPC-PPNNN realizes the nonparametric clustering process and makes the algorithm applicable for more complex datasets. In experiments on several datasets, DPC-PPNNN is shown to outperform DPC, K-means and DBSCAN.