make_biclusters (original) (raw)
sklearn.datasets.make_biclusters(shape, n_clusters, *, noise=0.0, minval=10, maxval=100, shuffle=True, random_state=None)[source]#
Generate a constant block diagonal structure array for biclustering.
Read more in the User Guide.
Parameters:
shapetuple of shape (n_rows, n_cols)
The shape of the result.
n_clustersint
The number of biclusters.
noisefloat, default=0.0
The standard deviation of the gaussian noise.
minvalfloat, default=10
Minimum value of a bicluster.
maxvalfloat, default=100
Maximum value of a bicluster.
shufflebool, default=True
Shuffle the samples.
random_stateint, RandomState instance or None, default=None
Determines random number generation for dataset creation. Pass an int for reproducible output across multiple function calls. See Glossary.
Returns:
Xndarray of shape shape
The generated array.
rowsndarray of shape (n_clusters, X.shape[0])
The indicators for cluster membership of each row.
colsndarray of shape (n_clusters, X.shape[1])
The indicators for cluster membership of each column.
See also
Generate an array with block checkerboard structure for biclustering.
References
[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.
Examples
from sklearn.datasets import make_biclusters data, rows, cols = make_biclusters( ... shape=(10, 20), n_clusters=2, random_state=42 ... ) data.shape (10, 20) rows.shape (2, 10) cols.shape (2, 20)