clustEval: Cluster-wise Clustering Robustness Evaluation
In hd2326/iterClust: Iterative Clustering

Description Usage Arguments Value Author(s) Examples

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.

1	clustEval(dset, iteration, clust)

`dset`	(numeric matrix) features in rows and observations in columns
`iteration`	(positive integer) specifies current iteration
`clust`	return value of coreClust

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

DING, HONGXU (hd2326@columbia.edu)

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)}