validateCluster {NIMAA}

Validate the cluster analysis in a projected network based on additional external measures.

Description

This function calculates the similarity of a given clustering method to the provided ground truth as external features (prior knowledge). This function provides external cluster validity measures including corrected.rand and ⁠jaccard similarity⁠. This function requires the community object, igraph object and distance matrix returned by findCluster to analyze.

Usage

validateCluster(community, extra_feature, dist_mat)

Arguments

community

An igraph community object.

extra_feature

A data frame object that shows the group membership of each node based on prior knowledge.

dist_mat

A matrix containing the distance of nodes in the network. This matrix can be retrieved by the output of findCluster to analyze.

Value

A list containing the similarity measures for the clustering results and the ground truth represented as an external features, i.e., corrected Rand and Jaccard indices.

Examples

# load part of the beatAML data
beatAML_data <- NIMAA::beatAML[1:10000,]

# convert to incidence matrix
beatAML_incidence_matrix <- nominalAsBinet(beatAML_data)

# do clustering
cls <- findCluster(beatAML_incidence_matrix,
  part = 1, method = c('infomap','walktrap'),
  normalization = FALSE, rm_weak_edges = TRUE,
  comparison = FALSE)

# generate a random external_feature
external_feature <- data.frame(row.names = cls$infomap$names)
external_feature[,'membership'] <- paste('group',
sample(c(1,2,3,4), nrow(external_feature),
replace = TRUE))

# validate clusters using random external feature
validateCluster(community = cls$walktrap,
extra_feature = external_feature,
dist_mat = cls$distance_matrix)