<commit_before>homepage: http://github.com/alpmestan/kmeans-vector
changelog-type: ''
hash: 520e0b5c0a8e67f3aa0daf4d4d0ec6c826a973edd244ba7795158865a3a63118
test-bench-deps:
  base: ! '>=4 && <5'
  criterion: -any
  QuickCheck: -any
  kmeans-vector: -any
  vector: ! '>=0.7'
maintainer: <NAME> <<EMAIL>>
synopsis: An implementation of the kmeans clustering algorithm based on the vector
  package
changelog: ''
basic-deps:
  base: ==4.*
  mtl: ! '>=2.1'
  QuickCheck: -any
  kmeans-vector: -any
  vector: ! '>=0.7'
all-versions:
- '0.1'
- '0.1.1'
- '0.2'
- '0.3'
author: <NAME> <<EMAIL>>, <NAME>
latest: '0.3'
description-type: haddock
description: ! 'Provides a simple (but efficient) implementation of the k-means clustering
  algorithm. The goal of this algorithm is to, given a set of n-dimensional points,
  regroup them in k groups, such that each point gets to be in the group to which
  it is the closest to (using the ''center'' of the group).


  CHANGELOG


  0.3: total rewrite of the code, the code scales much better on big inputs and is
  overall

  consistently faster than the other kmeans implementations on hackage, on my laptop.

  0.2: supports having feature vectors associated to objects, and thus computing kmeans
  on these vectors, letting you recover the initial objects.'
license-name: BSD3
<commit_msg>Update from Hackage at 2015-07-28T15:19:39+0000<commit_after>homepage: http://github.com/alpmestan/kmeans-vector
changelog-type: ''
hash: 5c75462fee6075ade28b47c5ce6b4601634389f72c777b0cb8d1498a8786f173
test-bench-deps:
  base: ! '>=4 && <5'
  criterion: -any
  QuickCheck: -any
  kmeans-vector: -any
  vector: ! '>=0.7'
maintainer: <NAME> <<EMAIL>>
synopsis: An implementation of the kmeans clustering algorithm based on the vector
  package
changelog: ''
basic-deps:
  base: ==4.*
  mtl: ! '>=2.1'
  probable: -any
  kmeans-vector: -any
  vector: ! '>=0.7'
all-versions:
- '0.1'
- '0.1.1'
- '0.2'
- '0.3'
- '0.3.1'
author: <NAME> <<EMAIL>>, <NAME>
latest: '0.3.1'
description-type: haddock
description: ! 'Provides a simple (but efficient) implementation of the k-means clustering
  algorithm. The goal of this algorithm is to, given a set of n-dimensional points,
  regroup them in k groups, such that each point gets to be in the group to which
  it is the closest to (using the ''center'' of the group).


  CHANGELOG


  0.3: total rewrite of the code, the code scales much better on big inputs and is
  overall

  consistently faster than the other kmeans implementations on hackage, on my laptop.

  0.2: supports having feature vectors associated to objects, and thus computing kmeans
  on these vectors, letting you recover the initial objects.'
license-name: BSD3

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