element_sim_elscore | R Documentation |
Calculates the element-wise element-centric similarity between two clustering results.
element_sim_elscore(
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
)
clustering1 |
The first clustering result, which can be one of:
|
clustering2 |
The second clustering result, which can be one of:
|
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:
|
ppr_implementation_cl1 |
Choose a implementation for personalized page-rank calculation for the first clustering:
|
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:
|
ppr_implementation_cl2 |
Choose a implementation for personalized page-rank calculation for the second clustering:
|
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. |
Vector of element-centric similarity between the two clusterings for each element.
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
km.res <- kmeans(iris[, 1:4], centers = 8)$cluster
hc.res <- hclust(dist(iris[, 1:4]))
element_sim_elscore(km.res, hc.res)
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