SpectralBiclustering: Performs clustering according to the spectral biclustering...

In rgudhi: An Interface to the GUDHI Library for Topological Data Analysis

Performs clustering according to the spectral biclustering algorithm

Description

This is a wrapper around the Python class sklearn.cluster.SpectralBiclustering.

Super classes

rgudhi::PythonClass -> rgudhi::SKLearnClass -> rgudhi::BaseClustering -> SpectralBiclustering

Methods

Public methods

• SpectralBiclustering$new()

• SpectralBiclustering$clone()

Inherited methods

• rgudhi::PythonClass$get_python_class()
• rgudhi::PythonClass$set_python_class()
• rgudhi::SKLearnClass$apply()
• rgudhi::SKLearnClass$fit()
• rgudhi::SKLearnClass$fit_transform()
• rgudhi::SKLearnClass$get_params()
• rgudhi::SKLearnClass$set_params()
• rgudhi::SKLearnClass$transform()

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Method new()

The SpectralBiclustering class constructor.

Usage

SpectralBiclustering$new(
  n_clusters = 3L,
  method = c("bistochastic", "scale", "log"),
  n_components = 6L,
  n_best = 3L,
  svd_method = c("randomized", "arpack"),
  n_svd_vecs = NULL,
  mini_batch = FALSE,
  init = c("k-means++", "random"),
  n_init = 10L,
  random_state = NULL
)

Arguments

n_clusters

An integer value or a length-2 vector specifying the number of row and column clusters in the checkerboard structure. Defaults to 3L.

method

A string specifying the 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". Warning: if method == "log", the data must not be sparse.

n_components

An integer value specifying the number of singular vectors to check. Defaults to 6L.

n_best

An integer value specifying the number of best singular vectors to which to project the data for clustering. Defaults to 3L.

svd_method

A string specifying the algorithm for finding singular vectors. May be "randomized" or "arpack". If "randomized", uses randomized_svd(), which may be faster for large matrices. If "arpack", uses scipy.sparse.linalg.svds(), which is more accurate, but possibly slower in some cases. Defaults to "randomized".

n_svd_vecs

An integer value specifying the number of vectors to use in calculating the SVD. Corresponds to ncv when svd_method == "arpack" and n_oversamples when svd_method == "randomized". Defaults to NULL.

mini_batch

A boolean value specifying whether to use mini-batch k-means, which is faster but may get different results. Defaults to FALSE.

init

A string specifying the method for initialization of k-means algorithm. Choices are "k-means++" or "random". Defaults to "k-means++".

n_init

An integer value specifying the 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. Defaults to 10L.

random_state

An integer value specifying a pseudo random number generator used for the initialization of the lobpcg eigenvectors decomposition when eigen_solver == "amg", and for the k-means initialization. Defaults to NULL which uses clock time.

Returns

An object of class SpectralBiclustering.

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Method clone()

The objects of this class are cloneable with this method.

Usage

SpectralBiclustering$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

cl <- SpectralBiclustering$new()

rgudhi documentation built on March 31, 2023, 11:38 p.m.