https://github.com/ayrat555/rock

Elixir implementation of ROCK: A Robust Clustering Algorithm for Categorical Attributes
https://github.com/ayrat555/rock

categorical-attributes cluster clustering clustering-algorithm elixir

Last synced: about 2 months ago
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Elixir implementation of ROCK: A Robust Clustering Algorithm for Categorical Attributes

• Host: GitHub
• URL: https://github.com/ayrat555/rock
• Owner: ayrat555
• License: mit
• Created: 2017-05-07T07:43:08.000Z (about 7 years ago)
• Default Branch: master
• Last Pushed: 2020-07-14T10:56:33.000Z (almost 4 years ago)
• Last Synced: 2024-03-01T09:49:33.582Z (4 months ago)
• Topics: categorical-attributes, cluster, clustering, clustering-algorithm, elixir
• Language: Elixir
• Size: 59.6 KB
• Stars: 11
• Watchers: 2
• Forks: 4
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGELOG.md
  • License: LICENSE

Lists

• awesome-elixir - rock - Elixir implementation of ROCK: A Robust Clustering Algorithm for Categorical Attributes. (Algorithms and Data structures)
• freaking_awesome_elixir - Elixir - Elixir implementation of ROCK: A Robust Clustering Algorithm for Categorical Attributes. (Algorithms and Data structures)
• awesome-elixir - rock - Elixir implementation of ROCK: A Robust Clustering Algorithm for Categorical Attributes. (Algorithms and Data structures)
• fucking-awesome-elixir - rock - Elixir implementation of ROCK: A Robust Clustering Algorithm for Categorical Attributes. (Algorithms and Data structures)

README

        
# 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](http://elixir-lang.org/getting-started/mix-otp/introduction-to-mix.html).

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

```elixir

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.

```elixir

  ## 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

1. [Fork it!](http://github.com/ayrat555/rock/fork)

2. Create your feature branch (`git checkout -b my-new-feature`)

3. Commit your changes (`git commit -am 'Add some feature'`)

4. Push to the branch (`git push origin my-new-feature`)

5. Create new Pull Request

## Author

Ayrat Badykov (@ayrat555)

## License

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