diversity_objective {anticlust} | R Documentation |
Compute the diversity for a given clustering.
diversity_objective(x, clusters)
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
|
clusters |
A vector representing (anti)clusters (e.g.,
returned by |
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.
The cluster editing objective
Martin Papenberg martin.papenberg@hhu.de
Brusco, M. J., Cradit, J. D., & Steinley, D. (in press). Combining diversity and dispersion criteria for anticlustering: A bicriterion approach. British Journal of Mathematical and Statistical Psychology. https://doi.org/10.1111/bmsp.12186
Papenberg, M., & Klau, G. W. (2020). Using anticlustering to partition data sets into equivalent parts. Psychological Methods. Advance Online Publication. https://doi.org/10.1037/met0000301.
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)