Description Usage Arguments Details Value Author(s) References

The Function will return the set of Gaussian Ellipsoids that best model the data

1 2 3 4 5 6 7 8 9 | ```
GMVECluster(dataset,
p.threshold=0.975,
samples=10000,
p.samplingthreshold=0.50,
sampling.rate = 3,
jitter=TRUE,
tryouts=25,
pca=TRUE,
verbose=FALSE)
``` |

`dataset` |
The data set to be clustered |

`p.threshold` |
The p-value threshold of point acceptance into a set. |

`samples` |
If the set is large, The number of random samples |

`p.samplingthreshold` |
Defines the maximum distance between set candidate points |

`sampling.rate` |
Uniform sampling rate for candidate clusters |

`jitter` |
If true, will jitter the data set |

`tryouts` |
The number of cluster candidates that will be analyed per sampled point |

`pca` |
If TRUE, it will use the PCA transform for dimension reduction |

`verbose` |
If true it will print the clustering evolution |

Implementation of the GMVE clustering algorithm as proposed by Jolion et al. (1991).

`cluster` |
The numeric vector with the cluster label of each point |

`classification` |
The numeric vector with the cluster label of each point |

`centers` |
The list of cluster centers |

`covariances` |
The list of cluster covariance |

`robCov` |
The list of robust covariances per cluster |

`k` |
The number of discovered clusters |

`features` |
The characer vector with the names of the features used |

`jitteredData` |
The jittered dataset |

Jose G. Tamez-Pena

Jolion, Jean-Michel, Peter Meer, and Samira Bataouche. "Robust clustering with applications in computer vision." IEEE Transactions on Pattern Analysis & Machine Intelligence 8 (1991): 791-802.

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