Infinite Loop in K-means when relocating empty clusters

### Describe the bug

Relocating empty clusters in Kmeans is not working as expected in this edge case, where :
- There is duplicate entries.
- The number of clusters is equal to the number of entries.
- Very particular initial positions.

Kmeans is stuck in a infinite loop, and the only way to end it is the max number of iterations. 

I have suggestions to improve the relocation of empty clusters : 
- When selecting an entry to fill an empty cluster (the entry that has max_dist), we should not select an entry in a cluster that contains only 1 entry.
- After each relocation of an empty clusters, Centers needs to be updated.

### Steps/Code to Reproduce

```python
import numpy as np
from sklearn.cluster import KMeans

X = np.array([
    [100, 100], [100, 102], [106, 100], [107, 101], [107, 101], [108, 100]
])
init = np.array([
    [98, 104], [103, 100], [109, 102], [110, 100], [111, 100], [112, 100]
])
kmeans = KMeans(
    n_clusters=6,
    init=init,
    random_state=0,
    verbose=True,
    algorithm='lloyd',
    max_iter=10_000
).fit(X)
```

### Expected Results

We should only perform 1 iteration

```
Initialization complete
Iteration 0, inertia 40.0.
Iteration 1, inertia 0.0.
```

### Actual Results

Kmeans is stuck in an infinite loop

```
Initialization complete
Iteration 0, inertia 40.0.
Iteration 1, inertia 0.0.
Iteration 2, inertia 0.0.
Iteration 3, inertia 0.0.
Iteration 4, inertia 0.0.
Iteration 5, inertia 0.0.
Iteration 6, inertia 0.0.
Iteration 7, inertia 0.0.
Iteration 8, inertia 0.0.
Iteration 9, inertia 0.0.
Iteration 10, inertia 0.0.
Iteration 11, inertia 0.0.
Iteration 12, inertia 0.0.
Iteration 13, inertia 0.0.
Iteration 14, inertia 0.0.
Iteration 15, inertia 0.0.
Iteration 16, inertia 0.0.
Iteration 17, inertia 0.0.
Iteration 18, inertia 0.0.
Iteration 19, inertia 0.0.
```

### Versions

```shell
System:
   python: 3.9.0
   machine: Windows-10

Python dependencies:
      sklearn: 1.3.2
          pip: 20.2.3
   setuptools: 49.2.1
        numpy: 1.25.1
        scipy: 1.11.4
       Cython: None
       pandas: 2.0.3
   matplotlib: 3.7.2
       joblib: 1.3.2
threadpoolctl: 3.2.0

Built with OpenMP: True

threadpoolctl info:
       user_api: blas
   internal_api: openblas
    num_threads: 12
         prefix: libopenblas
        version: 0.3.23
threading_layer: pthreads

       user_api: openmp
   internal_api: openmp
    num_threads: 12
         prefix: vcomp
        version: None

       user_api: blas
   internal_api: openblas
    num_threads: 12
         prefix: libopenblas
        version: 0.3.21.dev
threading_layer: pthreads
```

COMMENT:
@brmel I am entirely sure to understand what you are proposing here.

> When selecting an entry to fill an empty cluster (the entry that has max_dist), we should not select an entry in a cluster that contains only 1 entry.
> After each relocation of an empty clusters, Centers needs to be updated.

Debugging the `lloyd` loop, I can see that we have an "empty" centroid (the centroid for which we don't affect any sample) and this centroid will be changing label at each iteration which make the strict convergence fail. The non-strict convergence is also failing because between 2 iterations the change of label of the "empty" centroid implies a centroid shift.

I am not yet understanding what is the reason that the "empty" centroid is changing label (I thought that it was an `openmp` behaviour but the number of chunk is too low to even parallelize). @jeremiedbb will know for sure. I am pretty sure this is a corner case that we should be able to fix.

Labels: Bug