diversity_objective {anticlust} R Documentation

## (Anti)cluster editing "diversity" objective

### Description

Compute the diversity for a given clustering.

### Usage

```diversity_objective(x, clusters)
```

### Arguments

 `x` The data input. Can be one of two structures: (1) A data matrix where rows correspond to elements and columns correspond to features (a single numeric feature can be passed as a vector). (2) An N x N matrix dissimilarity matrix; can be an object of class `dist` (e.g., returned by `dist` or `as.dist`) or a `matrix` where the entries of the upper and lower triangular matrix represent the pairwise dissimilarities. `clusters` A vector representing (anti)clusters (e.g., returned by `anticlustering`).

### Details

The objective function used in (anti)cluster editing is the diversity, i.e., the sum of the pairwise distances between elements within the same groups. When the input `x` is a feature matrix, the Euclidean distance is computed as the basic distance unit of this objective.

### Value

The cluster editing objective

### Author(s)

Martin Papenberg martin.papenberg@hhu.de

### References

Brusco, M. J., Cradit, J. D., & Steinley, D. (2020). Combining diversity and dispersion criteria for anticlustering: A bicriterion approach. British Journal of Mathematical and Statistical Psychology, 73, 275-396. https://doi.org/10.1111/bmsp.12186

Papenberg, M., & Klau, G. W. (2020). Using anticlustering to partition data sets into equivalent parts. Psychological Methods, 26(2), 161–174. https://doi.org/10.1037/met0000301.

### Examples

```
data(iris)
distances <- dist(iris[1:60, -5])
## Clustering
clusters <- balanced_clustering(distances, K = 3)
# This is low:
diversity_objective(distances, clusters)
## Anticlustering
anticlusters <- anticlustering(distances, K = 3)
# This is higher:
diversity_objective(distances, anticlusters)

```

[Package anticlust version 0.6.0 Index]