New clustering metrics

### Describe the workflow you want to enable

Scikit-learn defines three popular metrics for evaluating clustering performance when there are no ground-truth cluster labels: [sklearn.metrics.silhouette_score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html), [sklearn.metrics.calinski_harabasz_score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.calinski_harabasz_score.html) and [sklearn.metrics.davies_bouldin_score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.davies_bouldin_score.html). But there are lots of others, and it's previously been [discussed](https://github.com/scikit-learn/scikit-learn/discussions/21164) whether to integrate more into scikit-learn.

### Describe your proposed solution

I've implemented four relatively popular ones, using the same interface and code style as in https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/metrics/cluster/_unsupervised.py. Would there be interest in integrating these into scikit-learn?

```python
import numpy as np
from itertools import combinations
from sklearn.utils import check_X_y
from sklearn.metrics.cluster._unsupervised import check_number_of_labels
from sklearn.preprocessing import LabelEncoder


def log_ss_ratio(X, labels):
    X, labels = check_X_y(X, labels)
    le = LabelEncoder()
    labels = le.fit_transform(labels)

    n_samples, _ = X.shape
    n_labels = len(le.classes_)

    check_number_of_labels(n_labels, n_samples)

    extra_disp, intra_disp = 0.0, 0.0
    mean = X.mean(axis=0)
    for k in range(n_labels):
        cluster_k = X[labels == k]
        mean_k = cluster_k.mean(axis=0)
        extra_disp += len(cluster_k) * ((mean_k - mean) ** 2).sum()
        intra_disp += ((cluster_k - mean_k) ** 2).sum()

    return np.log(extra_disp / intra_disp)


def ball_hall(X, labels):
    X, labels = check_X_y(X, labels)
    le = LabelEncoder()
    labels = le.fit_transform(labels)

    n_samples, _ = X.shape
    n_labels = len(le.classes_)

    check_number_of_labels(n_labels, n_samples)

    sum_mean_dispersions = 0

    for k in range(n_labels):
        cluster_k = X[labels == k]
        mean_k = cluster_k.mean(axis=0)
        intra_disp_cluster = ((cluster_k - mean_k) ** 2).sum()
        sum_mean_dispersions += intra_disp_cluster / len(cluster_k)

    return sum_mean_dispersions / n_labels


def banfeld_raftery(X, labels):
    X, labels = check_X_y(X, labels)
    le = LabelEncoder()
    labels = le.fit_transform(labels)

    n_samples, _ = X.shape
    n_labels = len(le.classes_)

    check_number_of_labels(n_labels, n_samples)

    br_index = 0
    for k in range(n_labels):
        cluster_k = X[labels == k]
        mean_k = cluster_k.mean(axis=0)
        intra_disp_cluster = ((cluster_k - mean_k) ** 2).sum()
        br_index += len(cluster_k) * \
                    np.log(intra_disp_cluster / len(cluster_k))

    return br_index


def ray_turi(X, labels):
    X, labels = check_X_y(X, labels)
    le = LabelEncoder()
    labels = le.fit_transform(labels)

    n_samples, _ = X.shape
    n_labels = len(le.classes_)

    check_number_of_labels(n_labels, n_samples)

    intra_disp = 0.0

    cluster_means = []
    for k in range(n_labels):
        cluster_k = X[labels == k]
        mean_k = cluster_k.mean(axis=0)
        intra_disp += ((cluster_k - mean_k) ** 2).sum()
        cluster_means.append(mean_k)

    min_cluster_mean_diff = min(
        ((mean_i - mean_j) ** 2).sum()
        for mean_i, mean_j in combinations(cluster_means, 2))

    return intra_disp / (min_cluster_mean_diff * n_samples)
```

### Describe alternatives you've considered, if relevant

_No response_

### Additional context

_No response_

COMMENT:
My personal experience: I tried out a bunch of these on single-cell RNA sequencing data, trying to select the optimal resolution hyperparameter for Leiden clustering. My experience was that Ray-Turi was the most reasonable, Davies-Bouldin and Calinski-Harabasz were way off, and Silhouette was relatively good but also ridiculously computationally expensive (also a problem that Dunn, Gap and Xie-Beni all suffer from). All of the metrics gave completely different answers from each other!

Labels: New Feature, Needs Decision - Include Feature