Math.Clustering.Hierarchical.Spectral.Sparse

Documentation

hierarchicalSpectralCluster :: EigenGroup -> NormalizeFlag -> Maybe NumEigen -> Maybe Int -> Maybe Q -> Maybe Runs -> Items a -> Either FeatureMatrix B -> IO (ClusteringTree a)

Generates a tree through divisive hierarchical clustering using Newman-Girvan modularity as a stopping criteria. Can use minimum number of observations in a cluster as a stopping criteria. Assumes the feature matrix has column features and row observations. Items correspond to rows. Can use FeatureMatrix or a pre-generated B matrix. See Shu et al., "Efficient Spectral Neighborhood Blocking for Entity Resolution", 2011.

hierarchicalSpectralClusterAdj :: EigenGroup -> Maybe NumEigen -> Maybe Int -> Maybe Q -> Maybe Runs -> Items a -> AdjacencyMatrix -> IO (ClusteringTree a)

Generates a tree through divisive hierarchical clustering using Newman-Girvan modularity as a stopping criteria. Can use minimum number of observations in a cluster as a stopping criteria. Uses an adjacency matrix. Items correspond to rows.

type FeatureMatrix = SpMatrix Double

newtype B

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

Constructors

B

Fields

• unB :: SpMatrix Double

Instances

Show B

Defined in Math.Clustering.Spectral.Sparse

Methods

showsPrec :: Int -> B -> ShowS

show :: B -> String

showList :: [B] -> ShowS

type Items a = Vector a

type ShowB = ((Int, Int), [(Int, Int, Double)])