Description Usage Arguments Value Author(s) Examples

Perform spherical k-means clustering on a data matrix. Similar to the k-means algorithm differing only in that data features are min-max normalized the dissimilarity metric is Cosine distance.

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

`centers` |
Either (i) The number of centers (i.e., k), or (ii) an In-memory data matrix |

`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 |

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

`tolerance` |
The convergence tolerance |

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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