clustEval: Cluster-wise Clustering Robustness Evaluation

Description Usage Arguments Value Author(s) Examples

View source: R/clustEval.R

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

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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

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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.