element_sim_matrix {ClustAssess}

Pairwise Comparison of Clusterings

Description

Compare a set of clusterings by calculating their pairwise average element-centric clustering similarities.

Usage

element_sim_matrix(
  clustering_list,
  output_type = "matrix",
  alpha = 0.9,
  r = 1,
  rescale_path_type = "max",
  ppr_implementation = "prpack",
  dist_rescaled = FALSE,
  row_normalize = TRUE,
  ncores = 1
)

Arguments

clustering_list

The list of clustering results, each of which is either:

• A numeric/character/factor vector of cluster labels for each element.

• A samples x clusters matrix/Matrix::Matrix of nonzero membership values.

• An hclust object.

output_type

A string specifying whether the output should be a matrix or a data.frame.

alpha

A numeric giving the personalized PageRank damping factor; 1 - alpha is the restart probability for the PPR random walk.

r

A numeric hierarchical scaling parameter.

rescale_path_type

A string; rescale the hierarchical height by:

• "max" : the maximum path from the root.

• "min" : the minimum path form the root.

• "linkage" : use the linkage distances in the clustering.

ppr_implementation

Choose a implementation for personalized page-rank calculation:

• "prpack": use PPR algorithms in igraph.

• "power_iteration": use power_iteration method.

dist_rescaled

A logical: if TRUE, the linkage distances are linearly rescaled to be in-between 0 and 1.

row_normalize

Whether to normalize all rows in clustering_result so they sum to one before calculating ECS. It is recommended to set this to TRUE, which will lead to slightly different ECS values compared to clusim.

ncores

the number of parallel R instances that will run the code. If the value is set to 1, the code will be run sequentially.

Value

A matrix or data.frame containing the pairwise ECS values.

References

Gates, A. J., Wood, I. B., Hetrick, W. P., & Ahn, Y. Y. (2019). Element-centric clustering comparison unifies overlaps and hierarchy. Scientific reports, 9(1), 1-13. https://doi.org/10.1038/s41598-019-44892-y

Examples

clustering.list = list()
for (i in 1:20) {
  clustering.list[[i]] = kmeans(mtcars, 3)$cluster
}
element_sim_matrix(clustering.list, output_type="matrix")