R: Element-Wise Consistency Between a Set of Clusterings

element_consistency {ClustAssess} | R Documentation

## Element-Wise Consistency Between a Set of Clusterings

### Description

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

### Usage

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

### 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.
`ncores` | the number of parallel R instances that will run the code. If the value is set to 1, the code will be run sequentially.

### Value

a vector containing the element-wise consistency.

### 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

    clustering.list = list()
    for (i in 1:20){
      clustering.list[[i]] = kmeans(mtcars, 3)$cluster
    }
    element_consistency(clustering.list)

[Package ClustAssess version 0.3.0 Index]