sklearn.datasets.make_biclusters

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

Generate an array with constant block diagonal structure for biclustering.

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, optional (default=None)

If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

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.

References

[R109]

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.