K-modes
Combine clustering results using K-modes.
k_modes(E, is.relabelled = TRUE, seed = 1)
E |
a matrix of clusterings with number of rows equal to the number of cases to be clustered, number of columns equal to the clustering obtained by different resampling of the data, and the third dimension are the different algorithms. Matrix may already be two-dimensional. |
is.relabelled |
logical; if |
seed |
random seed for reproducibility |
Combine clustering results generated using different algorithms and different
data perturbations by k-modes. This method is the categorical data analog of
k-means clustering. Complete cases are needed: i.e. no NAs. If the matrix
contains NAs those are imputed by majority voting (after class relabeling).
a vector of cluster assignments based on k-modes
Aline Talhouk
Other consensus functions:
CSPA(),
LCA(),
LCE(),
majority_voting()
data(hgsc) dat <- hgsc[1:100, 1:50] cc <- consensus_cluster(dat, nk = 4, reps = 6, algorithms = "pam", progress = FALSE) table(k_modes(cc[, , 1, 1, drop = FALSE], is.relabelled = FALSE))
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