R: Cluster comparison based on CVIs

cvi_evaluators {dtwclust} | R Documentation

## Cluster comparison based on CVIs

### Description

Create evaluation functions for `compare_clusterings()`.

### Usage

    cvi_evaluators(type = "valid", fuzzy = FALSE, ground.truth = NULL)

### Arguments

`type` | A character vector with options supported by `cvi()`.
`fuzzy` | Logical indicating whether to use fuzzy CVIs or not.
`ground.truth` | A vector that can be coerced to integers used for the calculation of external CVIs (passed as `b` to `cvi()`).

### Details

Think of this as a factory for `compare_clusterings()` that creates functions that can be passed as its `score.clus` and `pick.clus` arguments. It is somewhat limited in scope because it depends on the cluster validity indices available in `cvi()` for scoring and performs majority voting for picking. They always assume that no errors occurred.

The scoring function takes the CVIs that are to be minimized and "inverts" them by taking their reciprocal so that maximization can be considered. Its ellipsis (`...`) is passed to `cvi()`.

The picking function returns the best configuration if `return.objects` is `FALSE`, or a list with the chosen TSClusters object and the corresponding configuration otherwise.

Refer to the examples in `compare_clusterings()`.

### Value

A list with two functions: `score` and `pick`.

### Note

To avoid ambiguity, if this function is used, configurations for both fuzzy and crisp clusterings should not be provided in the same call to `compare_clusterings()`. In such cases the scoring function may fail entirely, e.g. if it was created with `type = "valid"`.

[Package dtwclust version 5.5.7 Index]