cluster.bicluster.SpectralCoclustering()

class sklearn.cluster.bicluster.SpectralCoclustering(n_clusters=3, svd_method='randomized', n_svd_vecs=None, mini_batch=False, init='k-means++', n_init=10, n_jobs=1, random_state=None) [source]

Spectral Co-Clustering algorithm (Dhillon, 2001).

Clusters rows and columns of an array X to solve the relaxed normalized cut of the bipartite graph created from X as follows: the edge between row vertex i and column vertex j has weight X[i, j].

The resulting bicluster structure is block-diagonal, since each row and each column belongs to exactly one bicluster.

Supports sparse matrices, as long as they are nonnegative.

Parameters:	n_clusters : integer, optional, default: 3 The number of biclusters to find. svd_method : string, optional, default: ?randomized? Selects the algorithm for finding singular vectors. May be ?randomized? or ?arpack?. If ?randomized?, use `sklearn.utils.extmath.randomized_svd`, which may be faster for large matrices. If ?arpack?, use `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.
Attributes:	rows_ : array-like, shape (n_row_clusters, n_rows) Results of the clustering. `rows[i, r]` is True if cluster `i` contains row `r`. Available only after calling `fit`. columns_ : array-like, shape (n_column_clusters, n_columns) Results of the clustering, like `rows`. row_labels_ : array-like, shape (n_rows,) The bicluster label of each row. column_labels_ : array-like, shape (n_cols,) The bicluster label of each column.

References

Dhillon, Inderjit S, 2001. Co-clustering documents and words using bipartite spectral graph partitioning.

Methods

`fit`(X)	Creates a biclustering for X.
`get_indices`(i)	Row and column indices of the i?th bicluster.
`get_params`([deep])	Get parameters for this estimator.
`get_shape`(i)	Shape of the i?th bicluster.
`get_submatrix`(i, data)	Returns the submatrix corresponding to bicluster `i`.
`set_params`(\\params)	Set the parameters of this estimator.

__init__(n_clusters=3, svd_method='randomized', n_svd_vecs=None, mini_batch=False, init='k-means++', n_init=10, n_jobs=1, random_state=None) [source]

biclusters_

Convenient way to get row and column indicators together.

Returns the rows_ and columns_ members.

fit(X) [source]

Creates a biclustering for X.

Parameters:	X : array-like, shape (n_samples, n_features)

get_indices(i) [source]

Row and column indices of the i?th bicluster.

Only works if rows_ and columns_ attributes exist.

Returns:

Returns:	row_ind : np.array, dtype=np.intp Indices of rows in the dataset that belong to the bicluster. col_ind : np.array, dtype=np.intp Indices of columns in the dataset that belong to the bicluster.

row_ind : np.array, dtype=np.intp

Indices of rows in the dataset that belong to the bicluster.

col_ind : np.array, dtype=np.intp

Indices of columns in the dataset that belong to the bicluster.

get_params(deep=True) [source]

Get parameters for this estimator.

Parameters:

Parameters:	deep : boolean, optional If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:	params : mapping of string to any Parameter names mapped to their values.

deep : boolean, optional

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

params : mapping of string to any

Parameter names mapped to their values.

get_shape(i) [source]

Shape of the i?th bicluster.

Returns:

Returns:	shape : (int, int) Number of rows and columns (resp.) in the bicluster.

shape : (int, int)

Number of rows and columns (resp.) in the bicluster.

get_submatrix(i, data) [source]

Returns the submatrix corresponding to bicluster i.

Works with sparse matrices. Only works if rows_ and columns_ attributes exist.

set_params(**params) [source]

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it?s possible to update each component of a nested object.

Returns:	self :

Links:

http://scikit-learn.org/stable/modules/generated/sklearn.cluster.bicluster.SpectralCoclustering.html

doc_scikit_learn

2017-01-15 04:20:39

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