hierarchical-spectral-clustering-0.4.1.1: Hierarchical spectral clustering of a graph.

Safe HaskellNone
LanguageHaskell2010

Math.Clustering.Hierarchical.Spectral.Dense

Synopsis

Documentation

hierarchicalSpectralCluster :: EigenGroup -> NormalizeFlag -> Maybe NumEigen -> Maybe Int -> Maybe Q -> Items a -> Either FeatureMatrix B -> ClusteringTree a Source #

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 :: Show a => EigenGroup -> Maybe NumEigen -> Maybe Int -> Maybe Q -> Items a -> AdjacencyMatrix -> ClusteringTree a Source #

Generates a tree through divisive hierarchical clustering using Newman-Girvan modularity as a stopping criteria. Can also use minimum number of observations in a cluster as the stopping criteria.

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

Instances
Show B 
Instance details

Defined in Math.Clustering.Spectral.Dense

Methods

showsPrec :: Int -> B -> ShowS #

show :: B -> String #

showList :: [B] -> ShowS #

type Items a = Vector a Source #

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