densityClust {densityClust} | R Documentation |

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.

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

`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 |

`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 |

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`

.

A densityCluster object. See details for a description.

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

```
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]