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Pairwise Comparison of Clusterings

Source: R/ECS.R
element_sim_matrix.Rd

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
)

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.

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

# cluster across 20 random seeds
clustering.list <- lapply(1:20, function(x) kmeans(mtcars, centers = 3)$cluster)
element_sim_matrix(clustering.list, output_type = "matrix")
#>            [,1]      [,2]      [,3]      [,4]      [,5]      [,6]      [,7]
#>  [1,] 1.0000000 0.5396205 0.5396205 1.0000000 0.5396205 1.0000000 1.0000000
#>  [2,] 0.5396205 1.0000000 1.0000000 0.5396205 1.0000000 0.5396205 0.5396205
#>  [3,] 0.5396205 1.0000000 1.0000000 0.5396205 1.0000000 0.5396205 0.5396205
#>  [4,] 1.0000000 0.5396205 0.5396205 1.0000000 0.5396205 1.0000000 1.0000000
#>  [5,] 0.5396205 1.0000000 1.0000000 0.5396205 1.0000000 0.5396205 0.5396205
#>  [6,] 1.0000000 0.5396205 0.5396205 1.0000000 0.5396205 1.0000000 1.0000000
#>  [7,] 1.0000000 0.5396205 0.5396205 1.0000000 0.5396205 1.0000000 1.0000000
#>  [8,] 1.0000000 0.5396205 0.5396205 1.0000000 0.5396205 1.0000000 1.0000000
#>  [9,] 0.5503724 0.7012775 0.7012775 0.5503724 0.7012775 0.5503724 0.5503724
#> [10,] 0.5503724 0.7012775 0.7012775 0.5503724 0.7012775 0.5503724 0.5503724
#> [11,] 0.5503724 0.7012775 0.7012775 0.5503724 0.7012775 0.5503724 0.5503724
#> [12,] 0.5396205 1.0000000 1.0000000 0.5396205 1.0000000 0.5396205 0.5396205
#> [13,] 0.5396205 1.0000000 1.0000000 0.5396205 1.0000000 0.5396205 0.5396205
#> [14,] 0.5503724 0.7012775 0.7012775 0.5503724 0.7012775 0.5503724 0.5503724
#> [15,] 0.5396205 1.0000000 1.0000000 0.5396205 1.0000000 0.5396205 0.5396205
#> [16,] 1.0000000 0.5396205 0.5396205 1.0000000 0.5396205 1.0000000 1.0000000
#> [17,] 1.0000000 0.5396205 0.5396205 1.0000000 0.5396205 1.0000000 1.0000000
#> [18,] 0.5396205 1.0000000 1.0000000 0.5396205 1.0000000 0.5396205 0.5396205
#> [19,] 0.5503724 0.7012775 0.7012775 0.5503724 0.7012775 0.5503724 0.5503724
#> [20,] 1.0000000 0.5396205 0.5396205 1.0000000 0.5396205 1.0000000 1.0000000
#>            [,8]      [,9]     [,10]     [,11]     [,12]     [,13]     [,14]
#>  [1,] 1.0000000 0.5503724 0.5503724 0.5503724 0.5396205 0.5396205 0.5503724
#>  [2,] 0.5396205 0.7012775 0.7012775 0.7012775 1.0000000 1.0000000 0.7012775
#>  [3,] 0.5396205 0.7012775 0.7012775 0.7012775 1.0000000 1.0000000 0.7012775
#>  [4,] 1.0000000 0.5503724 0.5503724 0.5503724 0.5396205 0.5396205 0.5503724
#>  [5,] 0.5396205 0.7012775 0.7012775 0.7012775 1.0000000 1.0000000 0.7012775
#>  [6,] 1.0000000 0.5503724 0.5503724 0.5503724 0.5396205 0.5396205 0.5503724
#>  [7,] 1.0000000 0.5503724 0.5503724 0.5503724 0.5396205 0.5396205 0.5503724
#>  [8,] 1.0000000 0.5503724 0.5503724 0.5503724 0.5396205 0.5396205 0.5503724
#>  [9,] 0.5503724 1.0000000 1.0000000 1.0000000 0.7012775 0.7012775 1.0000000
#> [10,] 0.5503724 1.0000000 1.0000000 1.0000000 0.7012775 0.7012775 1.0000000
#> [11,] 0.5503724 1.0000000 1.0000000 1.0000000 0.7012775 0.7012775 1.0000000
#> [12,] 0.5396205 0.7012775 0.7012775 0.7012775 1.0000000 1.0000000 0.7012775
#> [13,] 0.5396205 0.7012775 0.7012775 0.7012775 1.0000000 1.0000000 0.7012775
#> [14,] 0.5503724 1.0000000 1.0000000 1.0000000 0.7012775 0.7012775 1.0000000
#> [15,] 0.5396205 0.7012775 0.7012775 0.7012775 1.0000000 1.0000000 0.7012775
#> [16,] 1.0000000 0.5503724 0.5503724 0.5503724 0.5396205 0.5396205 0.5503724
#> [17,] 1.0000000 0.5503724 0.5503724 0.5503724 0.5396205 0.5396205 0.5503724
#> [18,] 0.5396205 0.7012775 0.7012775 0.7012775 1.0000000 1.0000000 0.7012775
#> [19,] 0.5503724 1.0000000 1.0000000 1.0000000 0.7012775 0.7012775 1.0000000
#> [20,] 1.0000000 0.5503724 0.5503724 0.5503724 0.5396205 0.5396205 0.5503724
#>           [,15]     [,16]     [,17]     [,18]     [,19]     [,20]
#>  [1,] 0.5396205 1.0000000 1.0000000 0.5396205 0.5503724 1.0000000
#>  [2,] 1.0000000 0.5396205 0.5396205 1.0000000 0.7012775 0.5396205
#>  [3,] 1.0000000 0.5396205 0.5396205 1.0000000 0.7012775 0.5396205
#>  [4,] 0.5396205 1.0000000 1.0000000 0.5396205 0.5503724 1.0000000
#>  [5,] 1.0000000 0.5396205 0.5396205 1.0000000 0.7012775 0.5396205
#>  [6,] 0.5396205 1.0000000 1.0000000 0.5396205 0.5503724 1.0000000
#>  [7,] 0.5396205 1.0000000 1.0000000 0.5396205 0.5503724 1.0000000
#>  [8,] 0.5396205 1.0000000 1.0000000 0.5396205 0.5503724 1.0000000
#>  [9,] 0.7012775 0.5503724 0.5503724 0.7012775 1.0000000 0.5503724
#> [10,] 0.7012775 0.5503724 0.5503724 0.7012775 1.0000000 0.5503724
#> [11,] 0.7012775 0.5503724 0.5503724 0.7012775 1.0000000 0.5503724
#> [12,] 1.0000000 0.5396205 0.5396205 1.0000000 0.7012775 0.5396205
#> [13,] 1.0000000 0.5396205 0.5396205 1.0000000 0.7012775 0.5396205
#> [14,] 0.7012775 0.5503724 0.5503724 0.7012775 1.0000000 0.5503724
#> [15,] 1.0000000 0.5396205 0.5396205 1.0000000 0.7012775 0.5396205
#> [16,] 0.5396205 1.0000000 1.0000000 0.5396205 0.5503724 1.0000000
#> [17,] 0.5396205 1.0000000 1.0000000 0.5396205 0.5503724 1.0000000
#> [18,] 1.0000000 0.5396205 0.5396205 1.0000000 0.7012775 0.5396205
#> [19,] 0.7012775 0.5503724 0.5503724 0.7012775 1.0000000 0.5503724
#> [20,] 0.5396205 1.0000000 1.0000000 0.5396205 0.5503724 1.0000000