Clustering by Fast Search and Find of Density Peaks

An improved implementation (based on k-nearest neighbors) of the density peak clustering algorithm, originally described by Alex Rodriguez and Alessandro Laio (Science, 2014 vol. 344). It can handle large datasets (> 100, 000 samples) very efficiently. It was initially implemented by Thomas Lin Pedersen, with inputs from Sean Hughes and later improved by Xiaojie Qiu to handle large datasets with kNNs.


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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 <- dist(iris[,1:4])
irisClust <- densityClust(irisDist, gaussian=TRUE)
plot(irisClust) # Inspect clustering attributes to define thresholds
 
irisClust <- findClusters(irisClust, rho=2, delta=2)
plotMDS(irisClust)
split(iris[,5], irisClust$clusters)

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

News

Reference manual

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install.packages("densityClust")

0.3 by Thomas Lin Pedersen, a year ago


Browse source code at https://github.com/cran/densityClust


Authors: Thomas Lin Pedersen [aut, cre] , Sean Hughes [aut] , Xiaojie Qiu [aut]


Documentation:   PDF Manual  


GPL (>= 2) license


Imports Rcpp, FNN, Rtsne, ggplot2, ggrepel, grDevices, gridExtra, RColorBrewer

Suggests testthat

Linking to Rcpp


See at CRAN