spectral-clustering-0.3.1.3: Library for spectral clustering.

Math.Clustering.Spectral.Dense

Synopsis

# Documentation

Returns the clustering of eigenvectors with the second smallest eigenvalues and on of the symmetric normalized Laplacian L. Computes real symmetric part of L, so ensure the input is real and symmetric. Diagonal should be 0s for adjacency matrix. Clusters the eigenvector using kmeans into k groups from e eigenvectors.

Returns the eigenvector with the second smallest eigenvalue of the symmetric normalized Laplacian L. Computes real symmetric part of L, so ensure the input is real and symmetric. Diagonal should be 0s for adjacency matrix.

Returns the eigenvectors with the Nth smallest eigenvalue and on of the symmetric normalized Laplacian L. Computes real symmetric part of L, so ensure the input is real and symmetric. Diagonal should be 0s for adjacency matrix.

Obtain the signed degree matrix.

Output vector containing cluster assignment (0 or 1).

newtype B Source #

Normed rows of B2. For a complete explanation, see Shu et al., "Efficient Spectral Neighborhood Blocking for Entity Resolution", 2011.

Constructors

 B FieldsunB :: Matrix Double
Instances
 Source # Instance detailsDefined in Math.Clustering.Spectral.Dense MethodsshowsPrec :: Int -> B -> ShowS #show :: B -> String #showList :: [B] -> ShowS #

newtype B1 Source #

B1 observation by feature matrix.

Constructors

 B1 FieldsunB1 :: Matrix Double
Instances
 Source # Instance detailsDefined in Math.Clustering.Spectral.Dense MethodsshowsPrec :: Int -> B1 -> ShowS #show :: B1 -> String #showList :: [B1] -> ShowS #

newtype B2 Source #

B2 term frequency-inverse document frequency matrix of B1.

Constructors

 B2 FieldsunB2 :: Matrix Double
Instances
 Source # Instance detailsDefined in Math.Clustering.Spectral.Dense MethodsshowsPrec :: Int -> B2 -> ShowS #show :: B2 -> String #showList :: [B2] -> ShowS #

spectral :: Int -> Int -> B -> [Vector Double] Source #

Returns the second left singular vector (or from N) and E on of a sparse spectral process. Assumes the columns are features and rows are observations. B is the normalized matrix (from getB). See Shu et al., "Efficient Spectral Neighborhood Blocking for Entity Resolution", 2011.

Returns a vector of cluster labels for two groups by finding the second left singular vector of a special normalized matrix. Assumes the columns are features and rows are observations. B is the normalized matrix (from getB). See Shu et al., "Efficient Spectral Neighborhood Blocking for Entity Resolution", 2011.

Returns a vector of cluster labels for two groups by finding the second left singular vector and on of a special normalized matrix and running kmeans. Assumes the columns are features and rows are observations. B is the normalized matrix (from getB). See Shu et al., "Efficient Spectral Neighborhood Blocking for Entity Resolution", 2011.

Get the normalized matrix B from an input matrix where the features are columns and rows are observations. Optionally, do not normalize.

Normalize the input matrix by column. Here, columns are features.

Get the cosine similarity between two rows using B2.