Description Usage Arguments Value Author(s) Examples

K-means provides **k** disjoint sets for a dataset using a parallel and fast
NUMA optimized version of Lloyd's algorithm. The details of which are found
in this paper https://arxiv.org/pdf/1606.08905.pdf.

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`data` |
Data file name on disk (NUMA optimized) or In memory data matrix |

`centers` |
Either (i) The number of centers (i.e., k), or |

`nrow` |
The number of samples in the dataset |

`ncol` |
The number of features in the dataset |

`iter.max` |
The maximum number of iteration of k-means to perform |

`nthread` |
The number of parallel threads to run
(ii) an In-memory data matrix, or (iii) A 2-Element |

`init` |
The type of initialization to use c("kmeanspp", "random", "forgy", "none") |

`tolerance` |
The convergence tolerance |

`dist.type` |
What dissimilarity metric to use |

A list containing the attributes of the output.
cluster: A vector of integers (from 1:**k**) indicating the cluster to
which each point is allocated.
centers: A matrix of cluster centres.
size: The number of points in each cluster.
iter: The number of (outer) iterations.

Disa Mhembere <[email protected]>

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clusternor documentation built on May 2, 2019, 11:36 a.m.

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