rock alternatives and similar packages
Based on the "Algorithms and Data structures" category.
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dataframe
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murmur
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WorkOS - The modern identity platform for B2B SaaS
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They vary from L1 to L5 with "L5" being the highest.
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Popular Comparisons
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.
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 clusterizednumber_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.
*Note that all licence references and agreements mentioned in the rock README section above
are relevant to that project's source code only.