clustEval: Cluster-wise Clustering Robustness Evaluation

In iterClust: Iterative Clustering

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 ([e-mail])

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)}

iterClust documentation built on Nov. 8, 2020, 5:43 p.m.