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Ai4r::Clusterers::WardLinkage

Implementation of an Agglomerative Hierarchical clusterer with Ward’s method linkage algorithm, aka the minimum variance method (Everitt et al., 2001 ; Jain and Dubes, 1988 ; Ward, 1963 ). Hierarchical clusteres create one cluster per element, and then progressively merge clusters, until the required number of clusters is reached. The objective of this method is to minime the variance.

D(cx, (ci U cj)) =  (ni/(ni+nj+nx))*D(cx, ci) + 
                    (nj/(ni+nj+nx))*D(cx, cj) - 
                    (nx/(ni+nj)^2)*D(ci, cj)

Public Instance Methods

build(data_set, number_of_clusters) click to toggle source

Build a new clusterer, using data examples found in data_set. Items will be clustered in “number_of_clusters” different clusters.

# File lib/ai4r/clusterers/ward_linkage.rb, line 38
def build(data_set, number_of_clusters)
  super
end
eval(data_item) click to toggle source

This algorithms does not allow classification of new data items once it has been built. Rebuild the cluster including you data element.

# File lib/ai4r/clusterers/ward_linkage.rb, line 44
def eval(data_item)
  Raise "Eval of new data is not supported by this algorithm."
end

Protected Instance Methods

linkage_distance(cx, ci, cj) click to toggle source

return distance between cluster cx and cluster (ci U cj), using ward’s method linkage

# File lib/ai4r/clusterers/ward_linkage.rb, line 52
def linkage_distance(cx, ci, cj)
  ni = @index_clusters[ci].length
  nj = @index_clusters[cj].length
  nx = @index_clusters[cx].length
  ( ( ( 1.0* (ni+nx) * read_distance_matrix(cx, ci) ) +
      ( 1.0* (nj+nx) * read_distance_matrix(cx, cj) ) ) / (ni + nj + nx)  -
      ( 1.0 * nx * read_distance_matrix(ci, cj) / (ni+nj)**2 ) ) 
end

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