Element-Wise Consistency Between a Set of Clusterings

Source: R/ECS.R

Inspect the consistency of a set of clusterings by calculating their element-wise clustering consistency (also known as element-wise frustration).

element_consistency(
  clustering_list,
  alpha = 0.9,
  r = 1,
  rescale_path_type = "max",
  ppr_implementation = "prpack",
  dist_rescaled = FALSE,
  row_normalize = TRUE,
  calculate_sim_matrix = FALSE
)

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.

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.

calculate_sim_matrix

A logical value that indicates whether to calculate the similarity matrix or not along with the consistency score. Defaults to FALSE.

Value

A vector containing the element-wise consistency. If calculate_sim_matrix is set to TRUE, the element similarity matrix will be returned as well.

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_consistency(clustering.list)
#>           Mazda RX4       Mazda RX4 Wag          Datsun 710      Hornet 4 Drive 
#>           0.6405960           0.6405960           0.8321594           0.5946685 
#>   Hornet Sportabout             Valiant          Duster 360           Merc 240D 
#>           0.7885851           0.5814684           0.7885851           0.8321594 
#>            Merc 230            Merc 280           Merc 280C          Merc 450SE 
#>           0.8321594           0.6405960           0.6405960           0.6580314 
#>          Merc 450SL         Merc 450SLC  Cadillac Fleetwood Lincoln Continental 
#>           0.6580314           0.6580314           0.7677527           0.7677527 
#>   Chrysler Imperial            Fiat 128         Honda Civic      Toyota Corolla 
#>           0.7677527           0.8321594           0.8321594           0.8321594 
#>       Toyota Corona    Dodge Challenger         AMC Javelin          Camaro Z28 
#>           0.8321594           0.6580314           0.6580314           0.7885851 
#>    Pontiac Firebird           Fiat X1-9       Porsche 914-2        Lotus Europa 
#>           0.7677527           0.8321594           0.8321594           0.8321594 
#>      Ford Pantera L        Ferrari Dino       Maserati Bora          Volvo 142E 
#>           0.7885851           0.6405960           0.7885851           0.8321594