8.1.2.3. sklearn.cluster.ward_tree

sklearn.cluster.ward_tree(X, connectivity=None, n_components=None, copy=True)

Ward clustering based on a Feature matrix.

The inertia matrix uses a Heapq-based representation.

This is the structured version, that takes into account a some topological structure between samples.

Parameters :

X : array of shape (n_samples, n_features)

feature matrix representing n_samples samples to be clustered

connectivity : sparse matrix.

connectivity matrix. Defines for each sample the neigbhoring samples following a given structure of the data. The matrix is assumed to be symmetric and only the upper triangular half is used. Default is None, i.e, the Ward algorithm is unstructured.

n_components : int (optional)

Number of connected components. If None the number of connected components is estimated from the connectivity matrix.

copy : bool (optional)

Make a copy of connectivity or work inplace. If connectivity is not of LIL type there will be a copy in any case.

Returns :

children : list of pairs. Lenght of n_nodes

list of the children of each nodes. Leaves of the tree have empty list of children.

n_components : sparse matrix.

The number of connected components in the graph.

n_leaves : int

The number of leaves in the tree

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