diff --git a/‎sklearn/calibration.py
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diff --git a/‎sklearn/cluster/_affinity_propagation.py
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diff --git a/‎sklearn/cluster/_agglomerative.py
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diff --git a/‎sklearn/cluster/_bicluster.py
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diff --git a/‎sklearn/cluster/_birch.py
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diff --git a/‎sklearn/cluster/_dbscan.py
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diff --git a/‎sklearn/cluster/_kmeans.py
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diff --git a/‎sklearn/cluster/_mean_shift.py
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diff --git a/‎sklearn/cluster/_optics.py
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diff --git a/‎sklearn/cluster/_spectral.py
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@@ -148,6 +148,12 @@ class CalibratedClassifierCV(ClassifierMixin, MetaEstimatorMixin, BaseEstimator)
 
         .. versionadded:: 0.24
 
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Only defined if the
+        underlying base_estimator exposes such an attribute when fit.
+
+        .. versionadded:: 1.0
+
     calibrated_classifiers_ : list (len() equal to cv or 1 if `cv="prefit"` \
             or `ensemble=False`)
         The list of classifier and calibrator pairs.
 
@@ -332,6 +332,12 @@ class AffinityPropagation(ClusterMixin, BaseEstimator):
 
         .. versionadded:: 0.24
 
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
     See Also
     --------
     AgglomerativeClustering : Recursively merges the pair of
 
@@ -812,6 +812,12 @@ class AgglomerativeClustering(ClusterMixin, BaseEstimator):
 
         .. versionadded:: 0.24
 
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
     children_ : array-like of shape (n_samples-1, 2)
         The children of each non-leaf node. Values less than `n_samples`
         correspond to leaves of the tree which are the original samples.
@@ -1098,6 +1104,12 @@ class FeatureAgglomeration(AgglomerativeClustering, AgglomerationTransform):
 
         .. versionadded:: 0.24
 
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
     children_ : array-like of shape (n_nodes-1, 2)
         The children of each non-leaf node. Values less than `n_features`
         correspond to leaves of the tree which are the original samples.
 
@@ -278,6 +278,12 @@ class SpectralCoclustering(BaseSpectral):
 
         .. versionadded:: 0.24
 
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
     Examples
     --------
     >>> from sklearn.cluster import SpectralCoclustering
@@ -427,6 +433,12 @@ class SpectralBiclustering(BaseSpectral):
 
         .. versionadded:: 0.24
 
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
     Examples
     --------
     >>> from sklearn.cluster import SpectralBiclustering
 
@@ -416,6 +416,12 @@ class Birch(ClusterMixin, TransformerMixin, BaseEstimator):
 
         .. versionadded:: 0.24
 
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
     See Also
     --------
     MiniBatchKMeans : Alternative implementation that does incremental updates
 
@@ -239,6 +239,12 @@ class DBSCAN(ClusterMixin, BaseEstimator):
 
         .. versionadded:: 0.24
 
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
     See Also
     --------
     OPTICS : A similar clustering at multiple values of eps. Our implementation
 
@@ -856,6 +856,12 @@ class KMeans(TransformerMixin, ClusterMixin, BaseEstimator):
 
         .. versionadded:: 0.24
 
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
     See Also
     --------
     MiniBatchKMeans : Alternative online implementation that does incremental
@@ -1620,6 +1626,12 @@ class MiniBatchKMeans(KMeans):
 
         .. versionadded:: 0.24
 
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
     See Also
     --------
     KMeans : The classic implementation of the clustering method based on the
 
@@ -331,6 +331,12 @@ class MeanShift(ClusterMixin, BaseEstimator):
 
         .. versionadded:: 0.24
 
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
     Examples
     --------
     >>> from sklearn.cluster import MeanShift
 
@@ -189,6 +189,12 @@ class OPTICS(ClusterMixin, BaseEstimator):
 
         .. versionadded:: 0.24
 
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
     See Also
     --------
     DBSCAN : A similar clustering for a specified neighborhood radius (eps).
 
@@ -447,6 +447,12 @@ class SpectralClustering(ClusterMixin, BaseEstimator):
 
         .. versionadded:: 0.24
 
+    feature_names_in_ : ndarray of shape (`n_features_in_`,)
+        Names of features seen during :term:`fit`. Defined only when `X`
+        has feature names that are all strings.
+
+        .. versionadded:: 1.0
+
     Examples
     --------
     >>> from sklearn.cluster import SpectralClustering