clustEval {iterClust}R Documentation

Cluster-wise Clustering Robustness Evaluation

Description

A sample cluster-wise clustering robustness evaluation framework (described in "Examples" section, used as default in iterClust framework). Customized frameworks can be defined following rules specified in "Usage", "Arguments" and "Value" sections.

Usage

clustEval(dset, iteration, clust)

Arguments

dset

(numeric matrix) features in rows and observations in columns

iteration

(positive integer) specifies current iteration

clust

return value of coreClust

Value

a numeric vector, specifies the clustering robustness (higher value means more robust) of each clustering scheme

Author(s)

DING, HONGXU (hd2326@columbia.edu)

Examples

clustEval <- function(dset, iteration, clust){
    dist <- as.dist(1 - cor(dset))
    clustEval <- vector("numeric", length(clust))
    for (i in 1:length(clust)){
        clustEval[i] <- mean(silhouette(clust[[i]], dist)[, "sil_width"])}
    return(clustEval)}


[Package iterClust version 1.15.0 Index]