ROCK

ROCK: A Robust Clustering Algorithm for Categorical Attributes

The algorithm's description http://theory.stanford.edu/~sudipto/mypapers/categorical.pdf

Installation

The easiest way to add Rock to your project is by using Mix.

Add :rock as a dependency to your project's mix.exs:

defp deps do
  [
    {:rock, "~> 0.1.2"}
  ]
end

And run:

$ mix deps.get

Basic Usage

To clusterize points using the Rock algorithm you should use Rock.clusterize/4 with the arguments:

points, points that will be clusterized
number_of_clusters, the number of desired clusters.
theta, neighborhood parameter in the range [0,1). Default value is 0.5.
similarity_function, distance function to use. Jaccard Coefficient is used by default.

  ## Examples

      points =
      [
        {"point1", ["1", "2", "3"]},
        {"point2", ["1", "2", "4"]},
        {"point3", ["1", "2", "5"]},
        {"point4", ["1", "3", "4"]},
        {"point5", ["1", "3", "5"]},
        {"point6", ["1", "4", "5"]},
        {"point7", ["2", "3", "4"]},
        {"point8", ["2", "3", "5"]},
        {"point9", ["2", "4", "5"]},
        {"point10", ["3", "4", "5"]},
        {"point11", ["1", "2", "6"]},
        {"point12", ["1", "2", "7"]},
        {"point13", ["1", "6", "7"]},
        {"point14", ["2", "6", "7"]}
      ]

      # Example 1

      Rock.clusterize(points, 5, 0.4)
      [
        [
          {"point4", ["1", "3", "4"]},
          {"point5", ["1", "3", "5"]},
          {"point6", ["1", "4", "5"]},
          {"point10", ["3", "4", "5"]},
          {"point7", ["2", "3", "4"]},
          {"point8", ["2", "3", "5"]}
        ],
        [
          {"point11", ["1", "2", "6"]},
          {"point12", ["1", "2", "7"]},
          {"point1", ["1", "2", "3"]},
          {"point2", ["1", "2", "4"]},
          {"point3", ["1", "2", "5"]}
        ],
        [
          {"point9", ["2", "4", "5"]}
        ],
        [
          {"point13", ["1", "6", "7"]}
        ],
        [
          {"point14", ["2", "6", "7"]}
        ]
      ]

      # Example 2 (with custom similarity function)

      similarity_function = fn(
          %Rock.Struct.Point{attributes: attributes1},
          %Rock.Struct.Point{attributes: attributes2}) ->

        count1 = Enum.count(attributes1)
        count2 = Enum.count(attributes2)

        if count1 >= count2, do: (count2 - 1) / count1, else: (count1 - 1) / count2
      end

      Rock.clusterize(points, 4, 0.5, similarity_function)
      [
        [
          {"point1", ["1", "2", "3"]},
          {"point2", ["1", "2", "4"]},
          {"point3", ["1", "2", "5"]},
          {"point4", ["1", "3", "4"]},
          {"point5", ["1", "3", "5"]},
          {"point6", ["1", "4", "5"]},
          {"point7", ["2", "3", "4"]},
          {"point8", ["2", "3", "5"]},
          {"point9", ["2", "4", "5"]},
          {"point10", ["3", "4", "5"]},
          {"point11", ["1", "2", "6"]}
        ],
        [
          {"point12", ["1", "2", "7"]}
        ],
        [
          {"point13", ["1", "6", "7"]}
        ],
        [
          {"point14", ["2", "6", "7"]}
        ]
      ]

Contributing

Fork it!
Create your feature branch (git checkout -b my-new-feature)
Commit your changes (git commit -am 'Add some feature')
Push to the branch (git push origin my-new-feature)
Create new Pull Request

Author

Ayrat Badykov (@ayrat555)

License

Rock is released under the MIT License. See the LICENSE file for further details.

ayrat555/rock

ayrat555

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