Combine clustering results using latent class analysis.

`LCA(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 LCA

Other consensus functions:
`CSPA()`

,
`LCE()`

,
`k_modes()`

,
`majority_voting()`

```
data(hgsc)
dat <- hgsc[1:100, 1:50]
cc <- consensus_cluster(dat, nk = 4, reps = 6, algorithms = "pam", progress =
FALSE)
table(LCA(cc[, , 1, 1, drop = FALSE], is.relabelled = FALSE))
#>
#> 1 2 3 4
#> 21 38 9 32
```