DensityPeakClustering: Density Peak Clustering algorithm using the Decision Graph

In FCPS: Fundamental Clustering Problems Suite

Density Peak Clustering algorithm using the Decision Graph

Description

Density peaks clustering of [Rodriguez/Laio, 2014] is here implemented by [Pedersen et al., 2017] with estimation of [Wang et al, 2015] meaning its non adaptive in the sense of ADPclustering.

Usage

DensityPeakClustering(DataOrDistances, Rho,Delta,Dc,Knn=7, 

DistanceMethod = "euclidean", PlotIt = FALSE, Data, ...)

Arguments

DataOrDistances	Either [1:n,1:n] symmetric distance matrix or [1:n,1:d] non symmetric data matrix of n cases and d variables
Rho	Local density of a point, see [Rodriguez/Laio, 2014] for explanation
Delta	Minimum distance between a point and any other point, see [Rodriguez/Laio, 2014] for explanation
Dc	Optional, cutoff distance, will either be estimated by [Pedersen et al., 2017] or [Wang et al, 2015] (see example below)
Knn	Optional k nearest neighbors
DistanceMethod	Optional distance method of data, default is euclid, see parDist for details
PlotIt	Optional TRUE: Plots 2d or 3d result with clustering
Data	[1:n,1:d] data matrix in the case that DataOrDistances is missing and partial matching does not work.
...	Optional, further arguments for densityClust

Details

The densityClust algorithm does not decide the k number of clusters, this has to be done by the parameter setting. This is contrary to the other version of the algorithm from another package which can be called with ADPclustering.

The plot shows the density peaks (Cluster centers). Set Rho and Delta as boundaries below the number of relevant cluster centers for your problem. (see example below).

Value

If Rho and Delta are set:

list of

Cls	[1:n numerical vector of numbers defining the classification as the main output of the clustering algorithm for the n cases of data. It has k unique numbers representing the arbitrary labels of the clustering.
Object	output of [Pedersen et al., 2017] algorithm

If Rho and Delta are missing:

p	object of plot_ly for the decision graph is returned

Author(s)

Michael Thrun

References

[Wang et al., 2015] Wang, S., Wang, D., Li, C., & Li, Y.: Comment on" Clustering by fast search and find of density peaks", arXiv preprint arXiv:1501.04267, 2015.

[Pedersen et al., 2017] Thomas Lin Pedersen, Sean Hughes and Xiaojie Qiu: densityClust: Clustering by Fast Search and Find of Density Peaks. R package version 0.3. https://CRAN.R-project.org/package=densityClust, 2017.

[Rodriguez/Laio, 2014] Rodriguez, A., & Laio, A.: Clustering by fast search and find of density peaks, Science, Vol. 344(6191), pp. 1492-1496. 2014.

See Also

ADPclustering

densityClust

Examples

data(Hepta)
H=EntropyOfDataField(Hepta$Data,  seq(from=0,to=1.5,by=0.05),PlotIt=FALSE)
Sigmamin=names(H)[which.min(H)]
Dc=3/sqrt(2)*as.numeric(names(H)[which.min(H)])
# Look at the plot and estimate rho and delta

DensityPeakClustering(Hepta$Data, Knn = 7,Dc=Dc)
Cls=DensityPeakClustering(Hepta$Data,Dc=Dc,Rho = 0.028,

Delta = 22,Knn = 7,PlotIt = TRUE)$Cls

FCPS documentation built on Oct. 19, 2023, 5:06 p.m.