A measure of similarity for the outputs of the different methods

Description

The function SimilarityMeasure computes the similarity of the methods. Given a list of outputs as input, the first element will be seen as the reference. Function MatrixFunction is called upon and the cluster numbers are rearranged according to the reference. Per method, SimilarityMeasure investigates which objects have the same cluster number in reference and said method. This number is divided by the total number of objects and used as a similarity measure.

Usage

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SimilarityMeasure(List, nrclusters = NULL, fusionsLog = TRUE, 
WeightClust = TRUE, names = NULL)

Arguments

List

A list of clustering outputs to be compared. The first element of the list will be used as the reference in ReorderToReference.

nrclusters

The number of clusters to cut the dendrogram in.~~

fusionsLog

To be handed to MatrixFunction.

WeightClust

To be handed to MatrixFunction.

names

Optional. Names of the methods.

Value

A vector of similarity measures, one for each method given as input.

Author(s)

Marijke Van Moerbeke

Examples

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data(fingerprintMat)
data(targetMat)

MCF7_F = Cluster(fingerprintMat,type="data",distmeasure="tanimoto",normalize=FALSE,
method=NULL,clust="agnes",linkage="ward",gap=FALSE,maxK=55,StopRange=FALSE)
MCF7_T = Cluster(targetMat,type="data",distmeasure="tanimoto",normalize=FALSE,
method=NULL,clust="agnes",linkage="ward",gap=FALSE,maxK=55,StopRange=FALSE)

L=list(MCF7_F,MCF7_T)
names=c("FP","TP")

MCF7_SimFandT=SimilarityMeasure(L,nrclusters=7,fusionsLog=TRUE,WeightClust=TRUE,
names=names)

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