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Kmeans Cluster

K-means clustering

What is K-means?

See also.

Tutorial

K-means clustering.

require 'kmeans/pair'
require 'kmeans/pearson'
require 'kmeans/cluster'

hash = {
  "test01"=> {"hoge"=>0, "fuga"=>1, "piyo"=>0 },
  "test02"=> {"hoge"=>2, "fuga"=>1, "piyo"=>3 },
  "test03"=> {"hoge"=>3, "fuga"=>0, "piyo"=>1 },
  "test04"=> {"hoge"=>0, "fuga"=>2, "piyo"=>0 },
  "test05"=> {"hoge"=>4, "fuga"=>2, "piyo"=>3 },
  "test06"=> {"hoge"=>3, "fuga"=>1, "piyo"=>1 }
}

kmeans = Kmeans::Cluster.new(hash, {
  :centroids => 3,
  :loop_max => 10
})

# Kmeans Clustering
kmeans.make_cluster

# The results differ for each run
p kmeans.cluster.values #=> [["test01", "test04"], ["test02"], ["test03", "test05", "test06"]]

Hierarchical clustering.

require 'kmeans/pair'
require 'kmeans/pearson'
require 'kmeans/hcluster'
require 'kmeans/dendrogram'

hash = {
  "test01"=> {"hoge"=>0, "fuga"=>1, "piyo"=>0 },
  "test02"=> {"hoge"=>2, "fuga"=>1, "piyo"=>3 },
  "test03"=> {"hoge"=>3, "fuga"=>0, "piyo"=>1 },
  "test04"=> {"hoge"=>0, "fuga"=>2, "piyo"=>0 },
  "test05"=> {"hoge"=>4, "fuga"=>2, "piyo"=>3 },
  "test06"=> {"hoge"=>5, "fuga"=>8, "piyo"=>2 },
  "test07"=> {"hoge"=>2, "fuga"=>6, "piyo"=>1 },
  "test08"=> {"hoge"=>3, "fuga"=>4, "piyo"=>1 },
  "test09"=> {"hoge"=>2, "fuga"=>2, "piyo"=>1 },
  "test10"=> {"hoge"=>1, "fuga"=>2, "piyo"=>1 }
}

# Hierarchical clustering
hcluster = Kmeans::HCluster.new

keys = hash.keys
values = hash.values.map {|x| x.values }
clust = hcluster.hcluster(values)
p hcluster.printclust(clust, keys) #=> "-\n -\n  -\n   test07\n   -\n    test01\n    test04\n  -\n   test08\n   -\n    test06\n    -\n     test09\n     test10\n -\n  test02\n  -\n   test03\n   test05\n"

# Draw Dendrogram (Using with RMagick)
dendrogram = Kmeans::Dendrogram.new(
  :imagefile => 'demo.png'
)

dendrogram.drawdendrogram(clust, keys) # Create demo.png on your directory

For more info, see spec files.

ChangeLog

See doc/ChangeLog.

Developers

See doc/AUTHORS.

Author

774

Copyright and license

See the file doc/LICENSE.

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