SingleLinkage
Implementation of an Agglomerative Hierarchical clusterer with centroid linkage algorithm, aka unweighted pair group method centroid (UPGMC) (Everitt et al., 2001 ; Jain and Dubes, 1988 ; Sokal and Michener, 1958 ) Hierarchical clusteres create one cluster per element, and then progressively merge clusters, until the required number of clusters is reached. The distance between clusters is the squared euclidean distance between their centroids.
D(cx, (ci U cj)) = | mx - mij |^2 D(cx, (ci U cj)) = (ni/(ni+nj))*D(cx, ci) + (nj/(ni+nj))*D(cx, cj) - (ni*nj/(ni+nj)^2)*D(ci, cj)
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/centroid_linkage.rb, line 41 def build(data_set, number_of_clusters) super end
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/centroid_linkage.rb, line 47 def eval(data_item) Raise "Eval of new data is not supported by this algorithm." end
return distance between cluster cx and cluster (ci U cj), using centroid linkage
# File lib/ai4r/clusterers/centroid_linkage.rb, line 55 def linkage_distance(cx, ci, cj) ni = @index_clusters[ci].length nj = @index_clusters[cj].length ( ni * read_distance_matrix(cx, ci) + nj * read_distance_matrix(cx, cj) - 1.0 * ni * nj * read_distance_matrix(ci, cj) / (ni+nj)) / (ni+nj) end
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