fpc (version 2.2-9)

randomclustersim: Simulation of validity indexes based on random clusterings

Description

For a given dataset this simulates random clusterings using stupidkcentroids, stupidknn, stupidkfn, and stupidkaven. It then computes and stores a set of cluster validity indexes for every clustering.

Usage

randomclustersim(datadist,datanp=NULL,npstats=FALSE,useboot=FALSE,
                      bootmethod="nselectboot",
                      bootruns=25, 
                      G,nnruns=100,kmruns=100,fnruns=100,avenruns=100,
                      nnk=4,dnnk=2,
                      pamcrit=TRUE, 
                      multicore=FALSE,cores=detectCores()-1,monitor=TRUE)

Arguments

datadist

distances on which validation-measures are based, dist object or distance matrix.

datanp

optional observations times variables data matrix, see npstats.

npstats

logical. If TRUE, distrsimilarity is called and the two statistics computed there are added to the output. These are based on datanp and require datanp to be specified.

useboot

logical. If TRUE, a stability index (either nselectboot or prediction.strength) will be involved.

bootmethod

either "nselectboot" or "prediction.strength"; stability index to be used if useboot=TRUE.

bootruns

integer. Number of resampling runs. If useboot=TRUE, passed on as B to nselectboot or M to prediction.strength.

G

vector of integers. Numbers of clusters to consider.

nnruns

integer. Number of runs of stupidknn.

kmruns

integer. Number of runs of stupidkcentroids.

fnruns

integer. Number of runs of stupidkfn.

avenruns

integer. Number of runs of stupidkaven.

nnk

nnk-argument to be passed on to cqcluster.stats.

dnnk

nnk-argument to be passed on to distrsimilarity.

pamcrit

pamcrit-argument to be passed on to cqcluster.stats.

multicore

logical. If TRUE, parallel computing is used through the function mclapply from package parallel; read warnings there if you intend to use this; it won't work on Windows.

cores

integer. Number of cores for parallelisation.

monitor

logical. If TRUE, it will print some runtime information.

Value

List with components

nn

list, indexed by number of clusters. Every entry is a data frame with nnruns observations for every simulation run of stupidknn. The variables of the data frame are avewithin, mnnd, cvnnd, maxdiameter, widestgap, sindex, minsep, asw, dindex, denscut, highdgap, pearsongamma, withinss, entropy, if pamcrit=TRUE also pamc, if npstats=TRUE also kdnorm, kdunif. All these are cluster validation indexes; documented as values of clustatsum.

fn

list, indexed by number of clusters. Every entry is a data frame with fnruns observations for every simulation run of stupidkfn. The variables of the data frame are avewithin, mnnd, cvnnd, maxdiameter, widestgap, sindex, minsep, asw, dindex, denscut, highdgap, pearsongamma, withinss, entropy, if pamcrit=TRUE also pamc, if npstats=TRUE also kdnorm, kdunif. All these are cluster validation indexes; documented as values of clustatsum.

aven

list, indexed by number of clusters. Every entry is a data frame with avenruns observations for every simulation run of stupidkaven. The variables of the data frame are avewithin, mnnd, cvnnd, maxdiameter, widestgap, sindex, minsep, asw, dindex, denscut, highdgap, pearsongamma, withinss, entropy, if pamcrit=TRUE also pamc, if npstats=TRUE also kdnorm, kdunif. All these are cluster validation indexes; documented as values of clustatsum.

km

list, indexed by number of clusters. Every entry is a data frame with kmruns observations for every simulation run of stupidkcentroids. The variables of the data frame are avewithin, mnnd, cvnnd, maxdiameter, widestgap, sindex, minsep, asw, dindex, denscut, highdgap, pearsongamma, withinss, entropy, if pamcrit=TRUE also pamc, if npstats=TRUE also kdnorm, kdunif. All these are cluster validation indexes; documented as values of clustatsum.

nnruns

number of involved runs of stupidknn,

fnruns

number of involved runs of stupidkfn,

avenruns

number of involved runs of stupidkaven,

kmruns

number of involved runs of stupidkcentroids,

boot

if useboot=TRUE, stability value; stabk for method nselectboot; mean.pred for method prediction.strength.

References

Hennig, C. (2019) Cluster validation by measurement of clustering characteristics relevant to the user. In C. H. Skiadas (ed.) Data Analysis and Applications 1: Clustering and Regression, Modeling-estimating, Forecasting and Data Mining, Volume 2, Wiley, New York 1-24, https://arxiv.org/abs/1703.09282

Akhanli, S. and Hennig, C. (2020) Calibrating and aggregating cluster validity indexes for context-adapted comparison of clusterings. Statistics and Computing, 30, 1523-1544, https://link.springer.com/article/10.1007/s11222-020-09958-2, https://arxiv.org/abs/2002.01822

See Also

stupidkcentroids, stupidknn, stupidkfn, stupidkaven, clustatsum

Examples

# NOT RUN {
  set.seed(20000)
  options(digits=3)
  face <- rFace(10,dMoNo=2,dNoEy=0,p=2)
  randomclustersim(dist(face),datanp=face,npstats=TRUE,G=2:3,
    nnruns=2,kmruns=2, fnruns=1,avenruns=1,nnk=2)
# }