R: The Element-Centric Clustering Similarity for each Element

element_sim_elscore {ClustAssess} | R Documentation

## The Element-Centric Clustering Similarity for each Element

### Description

Calculates the element-wise element-centric similarity between two clustering results.

### Usage

    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
    )

### 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

Vector of element-centric similarity between the two clusterings for each element.

### 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

    km.res = kmeans(iris[,1:4], centers=8)$cluster
    hc.res = hclust(dist(iris[,1:4]))
    element_sim_elscore(km.res, hc.res)

[Package ClustAssess version 0.3.0 Index]