element_sim: The Element-Centric Clustering Similarity

In Core-Bioinformatics/ClustAssess: Tools for Assessing Clustering

The Element-Centric Clustering Similarity

Description

Calculates the average element-centric similarity between two clustering results

Usage

element_sim(
  clustering1,
  clustering2,
  alpha = 0.9,
  r_cl1 = 1,
  rescale_path_type_cl1 = "max",
  ppr_implementation_cl1 = "prpack",
  dist_rescaled_cl1 = FALSE,
  row_normalize_cl1 = TRUE,
  r_cl2 = 1,
  rescale_path_type_cl2 = "max",
  ppr_implementation_cl2 = "prpack",
  dist_rescaled_cl2 = FALSE,
  row_normalize_cl2 = TRUE
)

Arguments

clustering1	The first clustering result, which can be one of: 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.
clustering2	The second clustering result, which can be one of: 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_cl1	A numeric hierarchical scaling parameter for the first clustering.
rescale_path_type_cl1	A string; rescale the hierarchical height of the first clustering 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_cl1	Choose a implementation for personalized page-rank calculation for the first clustering: "prpack": use PPR algorithms in igraph. "power_iteration": use power_iteration method.
dist_rescaled_cl1	A logical: if TRUE, the linkage distances of the first clustering are linearly rescaled to be in-between 0 and 1.
row_normalize_cl1	Whether to normalize all rows in the first clustering 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.
r_cl2	A numeric hierarchical scaling parameter for the second clustering.
rescale_path_type_cl2	A string; rescale the hierarchical height of the second clustering 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_cl2	Choose a implementation for personalized page-rank calculation for the second clustering: "prpack": use PPR algorithms in igraph. "power_iteration": use power_iteration method.
dist_rescaled_cl2	A logical: if TRUE, the linkage distances of the second clustering are linearly rescaled to be in-between 0 and 1.
row_normalize_cl2	Whether to normalize all rows in the second clustering 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

The average element-wise similarity between the two Clusterings.

Examples

km.res <- kmeans(mtcars, centers = 3)$cluster
hc.res <- hclust(dist(mtcars))
element_sim(km.res, hc.res)

Core-Bioinformatics/ClustAssess documentation built on Dec. 19, 2024, 8:19 p.m.