R: Detect clusters in a densityCluster obejct

findClusters {densityClust} | R Documentation

## Detect clusters in a densityCluster obejct

### Description

This function uses the supplied rho and delta thresholds to detect cluster peaks and assign the rest of the observations to one of these clusters. Furthermore core/halo status is calculated. If either rho or delta threshold is missing the user is presented with a decision plot where they are able to click on the plot area to set the treshold. If either rho or delta is set, this takes presedence over the value found by clicking.

### Usage

    findClusters(x, ...)

    ## S3 method for class 'densityCluster'
    findClusters(x, rho, delta, plot = FALSE,
      peaks = NULL, verbose = FALSE, ...)

### Arguments

`x` | A densityCluster object as produced by `densityClust()`
`...` | Additional parameters passed on
`rho` | The threshold for local density when detecting cluster peaks
`delta` | The threshold for minimum distance to higher density when detecting cluster peaks
`plot` | Logical. Should a decision plot be shown after cluster detection
`peaks` | A numeric vector indicates the index of density peaks used for clustering. This vector should be retrieved from the decision plot with caution. No checking involved.
`verbose` | Logical. Should the running details be reported

### Value

A densityCluster object with clusters assigned to all observations

### 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

### 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 Index]