Need help with densityClust?
Click the “chat” button below for chat support from the developer who created it, or find similar developers for support.

About the developer

thomasp85
125 Stars 64 Forks 43 Commits 3 Opened issues

Description

Clustering by fast search and find of density peaks

Services available

!
?

Need anything else?

Contributors list

# 15,431
R
ggplot2
ggplot-...
Shell
29 commits
# 226,486
R
Shell
data-vi...
TeX
10 commits
# 349,779
R
C++
Shell
1 commit
# 569,738
C++
R
1 commit

Clustering by fast search and find of density peaks

Travis-CI Build Status AppVeyor Build Status CRAN\_Release\_Badge CRAN\_Download\_Badge Coverage Status

This package implement the clustering algorithm described by Alex Rodriguez and Alessandro Laio (2014). It provides the user with tools for generating the initial rho and delta values for each observation as well as using these to assign observations to clusters. This is done in two passes so the user is free to reassign observations to clusters using a new set of rho and delta thresholds, without needing to recalculate everything.

Plotting

Two types of plots are supported by this package, and both mimics the types of plots used in the publication for the algorithm. The standard plot function produces a decision plot, with optional colouring of cluster peaks if these are assigned. Furthermore

plotMDS()
performs a multidimensional scaling of the distance matrix and plots this as a scatterplot. If clusters are assigned observations are coloured according to their assignment.

Cluster detection

The two main functions for this package are

densityClust()
and
findClusters()
. The former takes a distance matrix and optionally a distance cutoff and calculates rho and delta for each observation. The latter takes the output of
densityClust()
and make cluster assignment for each observation based on a user defined rho and delta threshold. If the thresholds are not specified the user is able to supply them interactively by clicking on a decision plot.

Usage

irisDist 

Note that while the iris dataset contains information on three different species of iris, only two clusters are detected by the algorithm. This is because two of the species (versicolor and virginica) are not clearly seperated by their data.

Refences

Rodriguez, A., & Laio, A. (2014). Clustering by fast search and find of density peaks. Science, 344(6191), 1492-1496. doi:10.1126/science.1242072

We use cookies. If you continue to browse the site, you agree to the use of cookies. For more information on our use of cookies please see our Privacy Policy.