densityClust-package | R Documentation |
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.
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.
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.
Maintainer: Thomas Lin Pedersen thomasp85@gmail.com
Authors:
Sean Hughes
Xiaojie Qiu xqiu@uw.edu
Rodriguez, A., & Laio, A. (2014). Clustering by fast search and find of density peaks. Science, 344(6191), 1492-1496. doi:10.1126/science.1242072
densityClust()
, findClusters()
, plotMDS()
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)
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