sklearn.cluster.bicluster Module

An sklearn.cluster.bicluster Module is an sklearn module that contains a collection of Spectral Biclustering Algorithms.

• Context:
  • It can (often) reference a sklearn.cluster.bicluster system.
    • sklearn.cluster.bicluster.Moldel_Name(self, arguments) or simply sklearn.cluster.bicluster.Model_Name()

      where Model_Name is the name of the selected Spectral Biclustering Algorithms.

• Example(s)
  • sklearn.cluster.bicluster.SpectralBiclustering.
  • sklearn.cluster.bicluster.SpectralCoclustering.
  • …
• Counter-Example(s):
  • sklearn.manifold, a collection of Manifold Learning Systems.
  • sklearn.tree, a collection of Decision Tree Learning Systems.
  • sklearn.ensemble, a collection of Decision Tree Ensemble Learning Systems.
  • sklearn.metrics, a collection of Metrics Subroutines.
  • sklearn.covariance,a collection of Covariance Estimators.
  • sklearn.linear_model, a collection of Linear Model Regression Systems.
  • sklearn.neighbors, a collection of K Nearest Neighbors Algorithms.
  • sklearn.neural_network, a collection of Neural Network Systems.
• See: Clustering System, Biclustering System.

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References

2017A

• (Scikit Learn, 2017) ⇒ http://scikit-learn.org/stable/modules/classes.html#module-sklearn.cluster.bicluster Retrieved:2017-11-12
  • QUOTE: Spectral biclustering algorithms.

    User guide: See the [Biclustering] section for further details.

    Classes

    • SpectralBiclustering([n_clusters, method, …]), Spectral biclustering (Kluger, 2003).
    • SpectralCoclustering([n_clusters, …]), Spectral Co-Clustering algorithm (Dhillon, 2001).

2017B

• (Scikit Learn, 2017) ⇒ http://scikit-learn.org/stable/modules/biclustering.html Retrieved:2017-11-12
  • QUOTE: Biclustering can be performed with the module sklearn.cluster.bicluster. Biclustering algorithms simultaneously cluster rows and columns of a data matrix. These clusters of rows and columns are known as biclusters. Each determines a submatrix of the original data matrix with some desired properties(...)

    Algorithms differ in how they define biclusters. Some of the common types include:

    • constant values, constant rows, or constant columns
    • unusually high or low values
    • submatrices with low variance
    • correlated rows or columns

Algorithms also differ in how rows and columns may be assigned to biclusters, which leads to different bicluster structures. Block diagonal or checkerboard structures occur when rows and columns are divided into partitions.

If each row and each column belongs to exactly one bicluster, then rearranging the rows and columns of the data matrix reveals the biclusters on the diagonal (...)

Note: Biclustering has many other names in different fields including co-clustering, two-mode clustering, two-way clustering, block clustering, coupled two-way clustering, etc. The names of some algorithms, such as the Spectral Co-Clustering algorithm, reflect these alternate names.

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