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# Author:: Sergio Fierens (implementation)
# License:: MPL 1.1
# Project:: ai4r
# Url::
# You can redistribute it and/or modify it under the terms of
# the Mozilla Public License version 1.1 as published by the
# Mozilla Foundation at
require File.dirname(__FILE__) + '/../data/data_set'
require File.dirname(__FILE__) + '/../clusterers/single_linkage'
module Ai4r
module Clusterers
# Implementation of a Hierarchical clusterer with group average
# linkage, AKA unweighted pair group method average or UPGMA (Everitt
# et al., 2001 ; Jain and Dubes, 1988 ; Sokal and Michener, 1958).
# Hierarchical clusterer create one cluster per element, and then
# progressively merge clusters, until the required number of clusters
# is reached.
# With average linkage, the distance between a clusters cx and
# cluster (ci U cj) the the average distance between cx and ci, and
# cx and cj.
# D(cx, (ci U cj) = (D(cx, ci) + D(cx, cj)) / 2
class AverageLinkage < SingleLinkage
parameters_info :distance_function =>
"Custom implementation of distance function. " +
"It must be a closure receiving two data items and return the " +
"distance between them. By default, this algorithm uses " +
"euclidean distance of numeric attributes to the power of 2."
# Build a new clusterer, using data examples found in data_set.
# Items will be clustered in "number_of_clusters" different
# clusters.
def build(data_set, number_of_clusters = 1, **options)
# This algorithms does not allow classification of new data items
# once it has been built. Rebuild the cluster including you data element.
def eval(data_item)
Raise "Eval of new data is not supported by this algorithm."
# return distance between cluster cx and cluster (ci U cj),
# using average linkage
def linkage_distance(cx, ci, cj)
(read_distance_matrix(cx, ci)+
read_distance_matrix(cx, cj))/2