densitypeakclustering 1.0.0

Implementation of the Clustering by fast search and find of density peaks algorithm

Density Peak Clustering

This project implements the 'Clustering by fast search and find of density peaks' algorithm as described by Rodriguez and Laio in their paper:

Rodriguez, Alex, and Alessandro Laio. “Clustering by Fast Search and Find of Density Peaks.” Science 344, no. 6191 (June 27, 2014): 1492–96 (https://www.science.org/doi/10.1126/science.1242072).

Overview

Density Peak Clustering is a clustering algorithm that identifies cluster centers by finding dense regions in the data and assigns the remaining points based on their distance to these centers. This project provides a custom implementation of this algorithm. The original code supplied with the paper ("cluster_dp.m", Matlab) can be found in the demo folder.

Features

• Compute local density and distance to higher density points
• Identify cluster centers
• Assign cluster IDs to each point
• Determine core samples of clusters

Demo's

The demo folder holds the following files:

1. demo_paper_figures.ipynb: Code to reproduce a selection of figures from the original paper using this python toolbox.
2. fig1.dat: Data point coordinates of figure 1 of the paper
3. fig2_panelB.dat: Data point coordinates of figure 2B of the paper
4. fig2_panelC.dat: Data point coordinates of figure 2C of the paper
5. cluster_dp.m: Original code from the authors of the paper

Installation

To use this project, clone the repository and install the required dependencies:

git clone https://github.com/pgoltstein/densitypeakclustering.git
cd densitypeakclustering
pip install .