Attempting to build a fast, memory efficient K-Means program.

gem sources -a http://rubygems.org
sudo gem install k_means

require 'rubygems'
require 'k_means'
data = [[1,1], [1,2], [1,1], [1000, 1000], [500, 500]]
kmeans = KMeans.new(data, :centroids => 2)
kmeans.inspect # Use kmeans.view to get hold of the un-inspected array
=> [[3, 4], [0, 1, 2]]

require 'rubygems'
require 'k_means'
# Your custom centroid needs to have #position and #reposition methods
class CustomCentroid
 attr_accessor :position
 def initialize(position); @position = position; end
 def reposition(nodes, centroid_positions); end
end
custom_centroids = []
2.times { custom_centroids << CustomCentroid.new([1,1]) }
data = [[1,1], [1,2], [1,1], [1000, 1000], [500, 500]]
kmeans = KMeans.new(data, :custom_centroids => custom_centroids)

KMeans uses the Distance Measures Gem (github.com/reddavis/Distance-Measures) so we get quite a range of distance measurements.

The measurements currently available are:

euclidean_distance
cosine_similarity
jaccard_index
jaccard_distance
binary_jaccard_index
binary_jaccard_distance
tanimoto_coefficient

To specify a particular one to use in the KMeans algorithm, just provide it as an option:

KMeans.new(@data, :distance_measure => :jaccard_index)
KMeans.new(@data, :distance_measure => :cosine_similarity)
KMeans.new(@data, :distance_measure => :tanimoto_coefficient)

You get the idea...

# 1000 records with 50 dimensions
data = Array.new(1000) {Array.new(50) {rand(10)}}
ai4r_data = Ai4r::Data::DataSet.new(:data_items=> data)
# Clustering can happen in magical ways
# so lets do it over multiple times
n = 5
Benchmark.bm do |x|
 x.report('KMeans') do
 n.times { KMeans.new(data) }
 end
 x.report("Ai4R") do
 n.times do
 b = Ai4r::Clusterers::KMeans.new
 b.build(ai4r_data, 4)
 end
 end
end
    user   system   total    real
KMeans 15.960000  0.030000 15.990000 ( 16.062639)
Ai4R 70.230000  0.180000 70.410000 ( 70.704843)

• David Richards - For his code reviews and all round helpfulness. - github.com/davidrichards

Copyright © 2009 Red Davis. See LICENSE for details.