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sklearn.datasets.make_biclusters

sklearn.datasets.make_biclusters(shape, n_clusters, noise=0.0, minval=10, maxval=100, shuffle=True, random_state=None) [source]

Generate an array with constant block diagonal structure for biclustering.

Read more in the User Guide.

Parameters:

Parameters:	`shape : iterable (n_rows, n_cols)` The shape of the result. `n_clusters : integer` The number of biclusters. `noise : float, optional (default=0.0)` The standard deviation of the gaussian noise. `minval : int, optional (default=10)` Minimum value of a bicluster. `maxval : int, optional (default=100)` Maximum value of a bicluster. `shuffle : boolean, optional (default=True)` Shuffle the samples. `random_state : int, RandomState instance or None (default)` Determines random number generation for dataset creation. Pass an int for reproducible output across multiple function calls. See Glossary.
Returns:	`X : array of shape shape` The generated array. `rows : array of shape (n_clusters, X.shape[0],)` The indicators for cluster membership of each row. `cols : array of shape (n_clusters, X.shape[1],)` The indicators for cluster membership of each column.

shape : iterable (n_rows, n_cols): The shape of the result.
n_clusters : integer: The number of biclusters.
noise : float, optional (default=0.0): The standard deviation of the gaussian noise.
minval : int, optional (default=10): Minimum value of a bicluster.
maxval : int, optional (default=100): Maximum value of a bicluster.
shuffle : boolean, optional (default=True): Shuffle the samples.
random_state : int, RandomState instance or None (default): Determines random number generation for dataset creation. Pass an int for reproducible output across multiple function calls. See Glossary.

Returns:

X : array of shape shape: The generated array.
rows : array of shape (n_clusters, X.shape[0],): The indicators for cluster membership of each row.
cols : array of shape (n_clusters, X.shape[1],): The indicators for cluster membership of each column.

[1]	Dhillon, I. S. (2001, August). Co-clustering documents and words using bipartite spectral graph partitioning. In Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 269-274). ACM.