Description Usage Arguments Value Examples

The clasical c-mean algorithm

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`data` |
A dataframe with only numerical variable |

`k` |
An integer describing the number of cluster to find |

`m` |
A float for the fuzziness degree |

`maxiter` |
A float for the maximum number of iteration |

`tol` |
The tolerance criterion used in the evaluateMatrices function for convergence assessment |

`standardize` |
A boolean to specify if the variables must be centered and reduced (default = True) |

`verbose` |
A boolean to specify if the messages should be displayed |

`init` |
A string indicating how the initial centers must be selected. "random" indicates that random observations are used as centers. "kpp" use a distance based method resulting in more dispersed centers at the beginning. Both of them are heuristic. |

`seed` |
An integer used for random number generation. It ensures that the start centers will be the same if the same integer is selected. |

A named list with :

Centers: a dataframe describing the final centers of the groups

Belongings: the final membership matrix

Groups: a vector with the names of the most likely group for each observation

Data: the dataset used to perform the clustering (might be standardized)

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