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Implements the Birch clustering algorithm. This node has been automatically generated by wrapping the ``sklearn.cluster.birch.Birch`` class from the ``sklearn`` library. The wrapped instance can be accessed through the ``scikits_alg`` attribute. Every new sample is inserted into the root of the Clustering Feature Tree. It is then clubbed together with the subcluster that has the centroid closest to the new sample. This is done recursively till it ends up at the subcluster of the leaf of the tree has the closest centroid. Read more in the :ref:`User Guide <birch>`. **Parameters** threshold : float, default 0.5 The radius of the subcluster obtained by merging a new sample and the closest subcluster should be lesser than the threshold. Otherwise a new subcluster is started. branching_factor : int, default 50 Maximum number of CF subclusters in each node. If a new samples enters such that the number of subclusters exceed the branching_factor then the node has to be split. The corresponding parent also has to be split and if the number of subclusters in the parent is greater than the branching factor, then it has to be split recursively. n_clusters : int, instance of sklearn.cluster model, default None Number of clusters after the final clustering step, which treats the subclusters from the leaves as new samples. By default, this final clustering step is not performed and the subclusters are returned as they are. If a model is provided, the model is fit treating the subclusters as new samples and the initial data is mapped to the label of the closest subcluster. If an int is provided, the model fit is AgglomerativeClustering with n_clusters set to the int. compute_labels : bool, default True Whether or not to compute labels for each fit. copy : bool, default True Whether or not to make a copy of the given data. If set to False, the initial data will be overwritten. **Attributes** ``root_`` : _CFNode Root of the CFTree. ``dummy_leaf_`` : _CFNode Start pointer to all the leaves. ``subcluster_centers_`` : ndarray, Centroids of all subclusters read directly from the leaves. ``subcluster_labels_`` : ndarray, Labels assigned to the centroids of the subclusters after they are clustered globally. ``labels_`` : ndarray, shape (n_samples,) Array of labels assigned to the input data. if partial_fit is used instead of fit, they are assigned to the last batch of data. **Examples** >>> from sklearn.cluster import Birch >>> X = [[0, 1], [0.3, 1], [0.3, 1], [0, 1], [0.3, 1], [0.3, 1]] >>> brc = Birch(branching_factor=50, n_clusters=None, threshold=0.5, ... compute_labels=True) >>> brc.fit(X) Birch(branching_factor=50, compute_labels=True, copy=True, n_clusters=None, threshold=0.5) >>> brc.predict(X) array([0, 0, 0, 1, 1, 1]) **References** * Tian Zhang, Raghu Ramakrishnan, Maron Livny BIRCH: An efficient data clustering method for large databases. http://www.cs.sfu.ca/CourseCentral/459/han/papers/zhang96.pdf * Roberto Perdisci JBirch  Java implementation of BIRCH clustering algorithm https://code.google.com/p/jbirch/














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_train_seq List of tuples: 

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supported_dtypes Supported dtypes 

Implements the Birch clustering algorithm. This node has been automatically generated by wrapping the ``sklearn.cluster.birch.Birch`` class from the ``sklearn`` library. The wrapped instance can be accessed through the ``scikits_alg`` attribute. Every new sample is inserted into the root of the Clustering Feature Tree. It is then clubbed together with the subcluster that has the centroid closest to the new sample. This is done recursively till it ends up at the subcluster of the leaf of the tree has the closest centroid. Read more in the :ref:`User Guide <birch>`. **Parameters** threshold : float, default 0.5 The radius of the subcluster obtained by merging a new sample and the closest subcluster should be lesser than the threshold. Otherwise a new subcluster is started. branching_factor : int, default 50 Maximum number of CF subclusters in each node. If a new samples enters such that the number of subclusters exceed the branching_factor then the node has to be split. The corresponding parent also has to be split and if the number of subclusters in the parent is greater than the branching factor, then it has to be split recursively. n_clusters : int, instance of sklearn.cluster model, default None Number of clusters after the final clustering step, which treats the subclusters from the leaves as new samples. By default, this final clustering step is not performed and the subclusters are returned as they are. If a model is provided, the model is fit treating the subclusters as new samples and the initial data is mapped to the label of the closest subcluster. If an int is provided, the model fit is AgglomerativeClustering with n_clusters set to the int. compute_labels : bool, default True Whether or not to compute labels for each fit. copy : bool, default True Whether or not to make a copy of the given data. If set to False, the initial data will be overwritten. **Attributes** ``root_`` : _CFNode Root of the CFTree. ``dummy_leaf_`` : _CFNode Start pointer to all the leaves. ``subcluster_centers_`` : ndarray, Centroids of all subclusters read directly from the leaves. ``subcluster_labels_`` : ndarray, Labels assigned to the centroids of the subclusters after they are clustered globally. ``labels_`` : ndarray, shape (n_samples,) Array of labels assigned to the input data. if partial_fit is used instead of fit, they are assigned to the last batch of data. **Examples** >>> from sklearn.cluster import Birch >>> X = [[0, 1], [0.3, 1], [0.3, 1], [0, 1], [0.3, 1], [0.3, 1]] >>> brc = Birch(branching_factor=50, n_clusters=None, threshold=0.5, ... compute_labels=True) >>> brc.fit(X) Birch(branching_factor=50, compute_labels=True, copy=True, n_clusters=None, threshold=0.5) >>> brc.predict(X) array([0, 0, 0, 1, 1, 1]) **References** * Tian Zhang, Raghu Ramakrishnan, Maron Livny BIRCH: An efficient data clustering method for large databases. http://www.cs.sfu.ca/CourseCentral/459/han/papers/zhang96.pdf * Roberto Perdisci JBirch  Java implementation of BIRCH clustering algorithm https://code.google.com/p/jbirch/




Transform X into subcluster centroids dimension. This node has been automatically generated by wrapping the sklearn.cluster.birch.Birch class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Each dimension represents the distance from the sample point to each cluster centroid. Parameters
Returns



Build a CF Tree for the input data. This node has been automatically generated by wrapping the sklearn.cluster.birch.Birch class from the sklearn library. The wrapped instance can be accessed through the scikits_alg attribute. Parameters

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