cluster.bicluster.SpectralBiclustering()

n_clusters : integer or tuple (n_row_clusters, n_column_clusters)

The number of row and column clusters in the checkerboard structure.

method : string, optional, default: ?bistochastic?

Method of normalizing and converting singular vectors into biclusters. May be one of ?scale?, ?bistochastic?, or ?log?. The authors recommend using ?log?. If the data is sparse, however, log normalization will not work, which is why the default is ?bistochastic?. CAUTION: if method=?log?, the data must not be sparse.

n_components : integer, optional, default: 6

Number of singular vectors to check.

n_best : integer, optional, default: 3

Number of best singular vectors to which to project the data for clustering.

svd_method : string, optional, default: ?randomized?

Selects the algorithm for finding singular vectors. May be ?randomized? or ?arpack?. If ?randomized?, uses sklearn.utils.extmath.randomized_svd, which may be faster for large matrices. If ?arpack?, uses sklearn.utils.arpack.svds, which is more accurate, but possibly slower in some cases.

n_svd_vecs : int, optional, default: None

Number of vectors to use in calculating the SVD. Corresponds to ncv when svd_method=arpack and n_oversamples when svd_method is ?randomized`.

mini_batch : bool, optional, default: False

Whether to use mini-batch k-means, which is faster but may get different results.

init : {?k-means++?, ?random? or an ndarray}

Method for initialization of k-means algorithm; defaults to ?k-means++?.

n_init : int, optional, default: 10

Number of random initializations that are tried with the k-means algorithm.

If mini-batch k-means is used, the best initialization is chosen and the algorithm runs once. Otherwise, the algorithm is run for each initialization and the best solution chosen.

n_jobs : int, optional, default: 1

The number of jobs to use for the computation. This works by breaking down the pairwise matrix into n_jobs even slices and computing them in parallel.

If -1 all CPUs are used. If 1 is given, no parallel computing code is used at all, which is useful for debugging. For n_jobs below -1, (n_cpus + 1 + n_jobs) are used. Thus for n_jobs = -2, all CPUs but one are used.

random_state : int seed, RandomState instance, or None (default)

A pseudo random number generator used by the K-Means initialization.