densityClust {densityClust} R Documentation

## Calculate clustering attributes based on the densityClust algorithm

### Description

This function takes a distance matrix and optionally a distance cutoff and calculates the values necessary for clustering based on the algorithm proposed by Alex Rodrigues and Alessandro Laio (see references). The actual assignment to clusters are done in a later step, based on user defined threshold values. If a distance matrix is passed into distance the original algorithm described in the paper is used. If a matrix or data.frame is passed instead it is interpretted as point coordinates and rho will be estimated based on k-nearest neighbors of each point (rho is estimated as exp(-mean(x)) where x is the distance to the nearest neighbors). This can be useful when data is so large that calculating the full distance matrix can be prohibitive.

### Usage

densityClust(distance, dc, gaussian = FALSE, verbose = FALSE, ...)


### Arguments

 distance A distance matrix or a matrix (or data.frame) for the coordinates of the data. If a matrix or data.frame is used the distances and local density will be estimated using a fast k-nearest neighbor approach. dc A distance cutoff for calculating the local density. If missing it will be estimated with estimateDc(distance) gaussian Logical. Should a gaussian kernel be used to estimate the density (defaults to FALSE) verbose Logical. Should the running details be reported ... Additional parameters passed on to get.knn

### Details

The function calculates rho and delta for the observations in the provided distance matrix. If a distance cutoff is not provided this is first estimated using estimateDc() with default values.

The information kept in the densityCluster object is:

rho

A vector of local density values

delta

A vector of minimum distances to observations of higher density

distance

The initial distance matrix

dc

The distance cutoff used to calculate rho

threshold

A named vector specifying the threshold values for rho and delta used for cluster detection

peaks

A vector of indexes specifying the cluster center for each cluster

clusters

A vector of cluster affiliations for each observation. The clusters are referenced as indexes in the peaks vector

halo

A logical vector specifying for each observation if it is considered part of the halo

knn_graph

kNN graph constructed. It is only applicable to the case where coordinates are used as input. Currently it is set as NA.

nearest_higher_density_neighbor

index for the nearest sample with higher density. It is only applicable to the case where coordinates are used as input.

nn.index

indices for each cell's k-nearest neighbors. It is only applicable for the case where coordinates are used as input.

nn.dist

distance to each cell's k-nearest neighbors. It is only applicable for the case where coordinates are used as input.

Before running findClusters the threshold, peaks, clusters and halo data is NA.

### Value

A densityCluster object. See details for a description.

### References

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

estimateDc(), findClusters()

### Examples

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)



[Package densityClust version 0.3.2 Index]