birch clustering does not work with version 1.1.1

### Describe the bug

Birch clustering does not work for version 1.1.1 but does work for version 0.24.1. This issue was also documented on StackOverflow [here](https://stackoverflow.com/questions/72560400/error-running-birch-from-scikit-learn-in-anaconda-environment)/

When trying to fit a dataframe in version 1.1.1 you get the error ValueError: ndarray is not C-contiguous. When running in version 0.24.1 it runs completely fine.

### Steps/Code to Reproduce

```python
from sklearn.datasets import load_iris
from sklearn.cluster import Birch

X, y = load_iris(return_X_y=True, as_frame=True)
birch = Birch(n_clusters=3)
birch.fit_predict(X)
```

### Expected Results

No error raised.

### Actual Results

```pytb
from sklearn.datasets import load_iris

from sklearn.cluster import Birch

​

X, y = load_iris(return_X_y=True, as_frame=True)

birch = Birch(n_clusters=3)

birch.fit_predict(X)

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Input In [4], in <cell line: 6>()
      4 X, y = load_iris(return_X_y=True, as_frame=True)
      5 birch = Birch(n_clusters=3)
----> 6 birch.fit_predict(X)

File ~/Documents/packages/scikit-learn/sklearn/base.py:716, in ClusterMixin.fit_predict(self, X, y)
    698 """
    699 Perform clustering on `X` and returns cluster labels.
    700 
   (...)
    712     Cluster labels.
    713 """
    714 # non-optimized default implementation; override when a better
    715 # method is possible for a given clustering algorithm
--> 716 self.fit(X)
    717 return self.labels_

File ~/Documents/packages/scikit-learn/sklearn/cluster/_birch.py:545, in Birch.fit(self, X, y)
    543 # TODO: Remove deprecated flags in 1.2
    544 self._deprecated_fit, self._deprecated_partial_fit = True, False
--> 545 return self._fit(X, partial=False)

File ~/Documents/packages/scikit-learn/sklearn/cluster/_birch.py:615, in Birch._fit(self, X, partial)
    612 self.subcluster_centers_ = centroids
    613 self._n_features_out = self.subcluster_centers_.shape[0]
--> 615 self._global_clustering(X)
    616 return self

File ~/Documents/packages/scikit-learn/sklearn/cluster/_birch.py:762, in Birch._global_clustering(self, X)
    759     self.subcluster_labels_ = clusterer.fit_predict(self.subcluster_centers_)
    761 if compute_labels:
--> 762     self.labels_ = self._predict(X)

File ~/Documents/packages/scikit-learn/sklearn/cluster/_birch.py:700, in Birch._predict(self, X)
    697 kwargs = {"Y_norm_squared": self._subcluster_norms}
    699 with config_context(assume_finite=True):
--> 700     argmin = pairwise_distances_argmin(
    701         X, self.subcluster_centers_, metric_kwargs=kwargs
    702     )
    703 return self.subcluster_labels_[argmin]

File ~/Documents/packages/scikit-learn/sklearn/metrics/pairwise.py:791, in pairwise_distances_argmin(X, Y, axis, metric, metric_kwargs)
    788         metric = "sqeuclidean"
    789         metric_kwargs = {}
--> 791     indices = PairwiseDistancesArgKmin.compute(
    792         X=X,
    793         Y=Y,
    794         k=1,
    795         metric=metric,
    796         metric_kwargs=metric_kwargs,
    797         strategy="auto",
    798         return_distance=False,
    799     )
    800     indices = indices.flatten()
    801 else:
    802     # TODO: once PairwiseDistancesArgKmin supports sparse input matrices and 32 bit,
    803     # we won't need to fallback to pairwise_distances_chunked anymore.
    804 
    805     # Turn off check for finiteness because this is costly and because arrays
    806     # have already been validated.

File ~/Documents/packages/scikit-learn/sklearn/metrics/_pairwise_distances_reduction/_dispatcher.py:233, in PairwiseDistancesArgKmin.compute(cls, X, Y, k, metric, chunk_size, metric_kwargs, strategy, return_distance)
    227 # Note (jjerphan): Some design thoughts for future extensions.
    228 # This factory comes to handle specialisations for the given arguments.
    229 # For future work, this might can be an entrypoint to specialise operations
    230 # for various backend and/or hardware and/or datatypes, and/or fused
    231 # {sparse, dense}-datasetspair etc.
    232 if X.dtype == Y.dtype == np.float64:
--> 233     return PairwiseDistancesArgKmin64.compute(
    234         X=X,
    235         Y=Y,
    236         k=k,
    237         metric=metric,
    238         chunk_size=chunk_size,
    239         metric_kwargs=metric_kwargs,
    240         strategy=strategy,
    241         return_distance=return_distance,
    242     )
    243 raise ValueError(
    244     "Only 64bit float datasets are supported at this time, "
    245     f"got: X.dtype={X.dtype} and Y.dtype={Y.dtype}."
    246 )

File ~/Documents/packages/scikit-learn/sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx:74, in sklearn.metrics._pairwise_distances_reduction._argkmin.PairwiseDistancesArgKmin64.compute()

File ~/Documents/packages/scikit-learn/sklearn/metrics/_pairwise_distances_reduction/_argkmin.pyx:343, in sklearn.metrics._pairwise_distances_reduction._argkmin.FastEuclideanPairwiseDistancesArgKmin64.__init__()

File ~/Documents/packages/scikit-learn/sklearn/metrics/_pairwise_distances_reduction/_datasets_pair.pyx:98, in sklearn.metrics._pairwise_distances_reduction._datasets_pair.DatasetsPair.get_for()

File ~/Documents/packages/scikit-learn/sklearn/metrics/_pairwise_distances_reduction/_datasets_pair.pyx:146, in sklearn.metrics._pairwise_distances_reduction._datasets_pair.DenseDenseDatasetsPair.__init__()

File stringsource:658, in View.MemoryView.memoryview_cwrapper()

File stringsource:349, in View.MemoryView.memoryview.__cinit__()

ValueError: ndarray is not C-contiguous
```

### Versions

```shell
System:
    python: 3.10.4 (main, Apr  2 2022, 09:04:19) [GCC 11.2.0]
executable: /home/creeg/projects/capstone/venv/bin/python
   machine: Linux-5.10.16.3-microsoft-standard-WSL2-x86_64-with-glibc2.35

Python dependencies:
          pip: 22.0.2
   setuptools: 59.6.0
      sklearn: 0.24.1
        numpy: 1.23.1
        scipy: 1.8.1
       Cython: None
       pandas: 1.4.2
   matplotlib: 3.5.2
       joblib: 1.1.0
threadpoolctl: 3.1.0

Built with OpenMP: True
```

COMMENT:
> I don't see how to make it efficient when it comes to cache CPU

I was thinking of converting the F-ordered arrays into C-ordered arrays in chucks. I suspect it would have worst performance compared to everything being C-ordered, but it would be more memory efficient. I do not know if it is worth doing since the code will end up being more complex.

In all cases, it would be nice to have an API to switch between "copy & new backend" and "no copy & old backend".

Labels: Bug