Description Usage Arguments Value Author(s)

Choose the number of clusters K that maximises the silhouette, starting from a set of kernel matrices each corresponding to a different choice of K and the corresponding clusterings of the data for each of those values of K.

1 2 3 4 5 6 7 8 9 10 11 |

`kernelMatrix` |
N X N X (maxK-1) array of kernel matrices. |

`clLabels` |
(maxK-1) X N matrix containing the clusterings obtained for different values of K. |

`maxK` |
Maximum number of clusters considered. |

`savePNG` |
If TRUE, a plot of the silhouette is saved in the working folder. Defaults to FALSE. |

`fileName` |
If |

`isDistance` |
Boolean. If TRUE, the kernel matrices are interpreted as matrices of distances, otherwise as matrices of similarities. |

`widestGap` |
Boolean. If TRUE, also computes widest gap index (and plots
it if |

`dunns` |
Boolean. If TRUE, also computes Dunn's index: minimum separation
/ maximum diameter (and plots it if |

`dunn2s` |
Boolean. If TRUE, also computes an alternative version
of Dunn's index: minimum average dissimilarity between two cluster / maximum
average within cluster dissimilarity (and plots it if |

The function returns a list containing:

`silh` |
a vector of length |

`K` |
the lowest number of clusters for which the silhouette is maximised. |

Alessandra Cabassi alessandra.cabassi@mrc-bsu.cam.ac.uk

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