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

`FALSE`

the data will be relabelled using the first clustering as the reference.- seed
random seed for reproducibility

a vector of cluster assignments based on k-modes

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 `NA`

s. If the matrix
contains `NA`

s those are imputed by majority voting (after class relabeling).

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))
#>
#> 1 2 3 4
#> 44 18 28 10
```