opt_kmedoids: Find optimal weights for Gower's metric and return the...
In Jeroentjeh/opthierarch: Hiearchical clustering with optimised Gower's metric's weights

Description Usage Arguments Details Value Note Author(s) References See Also Examples

This function is a wrapper function for find_kmedoids_weights that returns both the kmedoids clustering and the result from find_kmedoids_weights.

opt_kmedoids(data, k, start_values = rep(1 / ncol(data), ncol(data) - 1),
                        n_iterate = 10,
                        bounds=c(1 / (3 * ncol(data)),
                        1 - (ncol(data) - 1) / (3 * ncol(data))))

`data`	the data that needs to be clustered, provided as a dataframe or (numeric) matrix. It is assumed that rows correspond to instances and columns correspond to features.
`start_values`	a vector containing the initial values of the weights. Defaults to `1/ncol(data)` for all variables. They must not be negative. Furthermore, `1 - sum(start_values) >= bounds[1]` must hold. Due to the way the algorithm is programmed, you only have to supply values for the first ncol(data) - 1 variables!
`k`	the number of clusters, the K in K-medoids.
`n_iterate`	the maximum number of iterations used by the quasi-newton method `optim`. Defaults to 10.
`bounds`	a vector of size 2 containing the lower and upper bound in position 1 and 2 respectively. The lower bound must not be lower than 0 and not higher than `1/ncol(data)`. The upper bound will be set to the minimum of its current value and `1-(ncol(data)-1)*bounds[1]`. For more information, see details.

See find_kmedoids_weights for more details.

This function returns a list of two named lists:

`clustering`	the kmedoids clustering. See `pam` for more information.
`opt_result`	the results of the optimisation algorithm. See `find_kmedoids_weights` for more information.

This package requires the cluster package.

Jeroen van den Hoven

Clustering with optimised weights for Gower's metric: Using hierarchical clustering and Quasi-Newton methods to maximise the cophenetic correlation coefficient, Jeroen van den Hoven.

find_kmedoids_weights

## Basic example
data(mtcars)
find_kmedoids_weights(mtcars, k = 3)

## Using custom bounds
find_kmedoids_weights(mtcars, k = 3, bounds = c(0.05, 0.2))