Perform the training step of kernel k-means.

kkmeans(K, parameters)

## Arguments

K Kernel matrix. A list containing the number of clusters number_count.

## Value

This function returns a list containing:

clustering

the cluster labels for each element (i.e. row/column) of the kernel matrix.

objective

the value of the objective function for the given clustering.

parameters

same parameters as in the input.

## References

Gonen, M. and Margolin, A.A., 2014. Localized data fusion for kernel k-means clustering with application to cancer biology. In Advances in Neural Information Processing Systems (pp. 1305-1313).

## Examples

# Load one dataset with 100 observations, 2 variables, 4 clusters
parameters$cluster_count <- 4 # Perform training state <- kkmeans(km, parameters) # Display the clustering print(state$clustering)#>   [1] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2 2 2 2 2 2 2 2 2
#>  [75] 4 1 1 1 1 1 1 1 1 4 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1